Doctor of Philosophy Electronics and Communication Engineering

Analytical Studies Relating ToBandwidth Extension of Speech Signal

for Next Generation WirelessCommunication

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Electronics and Communication Engineering

by

Rajnikant Natubhai Rathod

159997111013

under supervision of

Dr. Mehfuza S. Holia

GUJARAT TECHNOLOGICALUNIVERSITYAHMEDABAD

July-2021

ii

© Rajnikant Natubhai Rathod

iii

I declare that the thesis entitled Analytical Studies Relating To Bandwidth

Extension of Speech Signal for Next Generation Wireless Communication

submitted by me for the degree of Doctor of Philosophy is the record of research

work carried out by me during the period from 2015 to 2021 under the supervision

of Dr. Mehfuza S. Holia and this has not formed the basis for the award of any

degree, diploma, associateship, fellowship, titles in this or any other University or

other institution of higher learning.

I further declare that the material obtained from other sources has been duly

acknowledged in the thesis. I shall be solely responsible for any plagiarism

or other irregularities, if noticed in the thesis.

Signature of the Research Scholar:......................................... Date: 02/07/ 2021

Name of Research Scholar: Rajnikant Natubhai Rathod

Place:LEC,Morbi

iv

CERTIFICATE

I certify that the work incorporated in the thesis Analytical Studies Relating To

Bandwidth Extension of Speech Signal for Next Generation Wireless

Communication submitted by Rajnikant Natubhai Rathod was carried out by

the candidate under my supervision/guidance. To the best of my knowledge: (i) the

candidate has not submitted the same research work to any other institution for any

degree/diploma, Associateship, Fellowship or other similar titles (ii) the thesis

submitted is a record of original research work done by the Research Scholar during

the period of study under my supervision, and (iii) the thesis represents independent

research work on the part of the Research Scholar.

Signature of Supervisor: .............................................. Date: 02/07/ 2021

Name of Supervisor: Dr. Mehfuza S. Holia

Place: BVM,V.V.Nagar

v

Course-work Completion Certificate

This is to certify that Mr. Rajnikant Natubhai Rathod enrolment

no.159997111013 is a PhD scholar enrolled for PhD program in the branch

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Ahmedabad.

(Please tick the relevant option(s))

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He/She has successfully completed the PhD course work for the partial

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Grade Obtained in ResearchMethodology (PH001)

Grade Obtained in Self Study Course(Core Subject)

(PH002)

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Sign

(Dr. Mehfuza S. Holia)

vi

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URL: https://ijcsmc.com/docs/papers/October2013/V2I10201318.pdf

viii

PhD THESIS Non-Exclusive License to

GUJARAT TECHNOLOGICAL UNIVERSITY

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Seal: Dr. M.S.Holia

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BVM, V.V.Nagar

Thesis Approval Form

The viva-voce of the PhD Thesis submitted by Shri

Rajnikant Natubhai Rathod (Enrollment No. 159997111013) entitled Analytical

Studies Relating To Bandwidth Extension of Speech Signal for Next Generation

Wireless Communication was conducted on Friday(02-07-2021) at Gujarat

Technological University.

(Please tick any one of the following option)

The performance of the candidate was satisfactory. We recommend that

he/she should be awarded the PhD degree.

Any further modifications in research work recommended by the panel

after 3 months from the date of first viva-voce upon request of the

Supervisor or request of Independent Research Scholar after which

viva-voce can be re-conducted by the same panel again.

(briefly specify the modifications suggested by the panel)

The performance of the candidate was unsatisfactory. We

recommend that he/she should not be awarded the PhD degree.

(The panel must give justifications for rejecting the research work)

----------------------------------------------------- ----------------------------------------------

Name and Signature of Supervisor with Seal 1) (External Examiner 1) Name andSignature

------------------------------------------------------- ----------------------------------------------

2) (External Examiner 2) Name and Signature 3) (External Examiner 3) Name andSignature

x

x

Dr Pancham Shukla

K.R.Parmar

xi

ABSTRACT

As the constraint of the Wireless transmission frequency band resources due to the

complexity of the 3G mobile communications environment, reconstructed Speech (voice)

signal quality, intelligibility at the receiver side is found stiffened, barely audible, slim.In

contrast, Today's terminals, infrastructure, operate at wider bandwidths for which speech

(voice) quality, intelligibility are greatly improved. Normally, the total move from

narrowband (300-3.4KHz) to wideband (0.05-7kHz) and wideband (0.05-7kHz) to super-

wideband (0.05-14kHz) communications will require impressive time. As a result,

wideband, super-wideband innovation must interoperate with narrowband innovation. In

this case, clients will involvement significant varieties in speech (voice) quality and

intelligibility.

The strategy of accomplishing the original wideband signal from a band-limited

narrowband speech signal without actually transmitting the wideband signal is called

bandwidth extension. Bandwidth extension aims to recreate wideband speech (0-8kHz)

from a narrowband speech signal (0-4kHz). Significant improvements in the quality of

speech coders have been achieved by widening the coded frequency range from

narrowband to wideband. The same concept can be applied for attaining super wideband

from the wideband signal. Bandwidth extension based on sinusoidal transform coding,

linear prediction, non-linear device gives good results compared to spectral

folding/spectral translation approaches employed by various researchers and academicians.

Within the modern state of undertakings of progression in innovation, different

coding algorithms have been built-up for super wideband to obtain the full benefit of

advancement in available telecommunications bandwidth, predominantly for the internet.

Think about based on sub-band filter has been examined where the work of sub-band filter

is to part out the wideband speech into low frequency and high-frequency parts, giving out

both parts exclusively so that without a wideband coder at the transmitter side the near-

perfect unique wideband speech (voice) quality and intelligibility has been recovered. The

obtained voice signal quality and intelligibility can be further processed via source filter

model based on linear prediction. For assessing speech (voice) quality, intelligibility,

frequency domain spectrogram analysis has been carried out on speech (voice) signal to

judge the speech (voice) signal compression. From the obtained speech (voice) signal at

the time-varying synthesis filter, it can be concluded that the source-filter model requires

xii

fewer bits compared to original speech (voice) signal transmission at the cost of

degradation in speech quality. One can notice the significant changes in original speech

(voice) quality by observing the spectrogram of speech (voice) signal. Less number of bit

representation can help in reducing the storage requirement, bandwidth.

Within the proposed strategy based on the source-filter demonstrate, deep-seated

thought adopted for the bandwidth extensions are the separate extensions of the spectral

envelope and residual signal. Each part is processed independently through different

speech (voice) enrichment procedure to get the high band component,. It is then added to

the re-sampled and delayed version of the signal to acquire the final extended output.

Obtained output is compared through intelligibility (subjective), quality perspective

(objective) and results for both the analysis are compared with baseline algorithm, next-

generation super wideband coder algorithms to prove that obtained results are comparable

with both algorithms. Algebraic evaluation to get missing high band components from the

original Narrowband/Wideband signal is not needed as in this approaches, the

Wideband/Super wideband signal achieved by Bandwidth extension from N.B./W.B.

signal itself. Multimedia communication, DSVD, VOIP, Voice messaging, Speaker

recognition, Internet telephony, Tele-conferencing are some of the important areas of

bandwidth extension. The research work in this thesis may help to shed light on the

proposed S.F.M. based algorithm which can be an alternative for the next generation super

wideband E.V.S. coder employed for speech(voice) quality, intelligibility improvement.

S.W.B. to F.B. speech quality is also the next targeted area of work for researchers found

interest in the area of speech coding and processing. The proposed work can also be

extended for music signals and music and mixed (speech and music).

xiii

Acknowledgement and/or Dedication

First I would like to convey my utmost gratitude towards my mentor god, Lord Krishna for

all his guidance and provide me courage, ability to complete this research work.

I am deeply gratified to have a supervisor like Dr. M. S. Holia, Assistant Professor,

Electronics Department, B.V.M. Engineering College V.V.Nagar-Anand. His continuous

encouragement, suggestions, advice, guidance inspires me to work continuously, improvise it.

I would like to thank him for being my advisor & supporter for research work. His

insightful remarks, encouragement have been of great to my research work as well as to

my future career.

I am very thankful to Doctoral Progress Committee (DPC): Dr. T. D. Pawar, Head,

Electronics Department, B.V.M. Engineering College V.V.Nagar-Anand, Dr. V. K.

Thakar, Principal, Hansaba college of Engineering & Technology, Siddhpur for their

valuable suggestions, support during reviews. I am very grateful for their suggestions and

critical reviews that enable me to improve this research work.

I am very thankful to Dr. Indrajit Patel, Principal BVM engineering college, Dr. S. N.

Pandya, Principal L.E. College, Prof. M. V. Makwana, H.O.D. Power Electronics

Department, Prof. M. H. Ayalani, Associate Professor, Power Electronics, for their

valuable support, guidance throughout my research work.

I am very thankful to my parents, brothers for all their support during the tenure of my

research. Meera, my wife who always stood by my side in all difficult time of my life,

provided support, encouragement to complete research work. I am very grateful to have loving

son Jayveer who have to lighten my mood during difficult times.

I would like to thank Prof. A. C. Lakum, Prof. V. J. Rupapara, Prof. P. D. Raval, Prof.

H. T. Loriya, Prof. H. M. Karkar, Prof. B. J. Makwana, Prof. A. P. Patel, Prof. J. B.

Bheda, Prof. A. R. Gauswami, Prof. S. N. Gohil for providing assistance whenever I

face difficulties in work. Last but not least all of those who have helped in finishing

research work.

Rajnikant Natubhai Rathod

Research Scholar, Gujarat Technological University

xiv

Table of Contents

Abstract ................................................................................................................................. xi

Acknowledgement ............................................................................................................. xiii

CHAPTER-1 .......................................................................................................................... 1

Introduction ............................................................................................................................ 1

1.1 Fruition of Communication Systems ...................................................................... 1

1.1.1 Analog, Digital telephony.................................................................................1

1.1.2 Wireless Cellular Networks .............................................................................. 1

1.1.3 Speech Coding .................................................................................................. 3

1.1.4 Background on Digital Speech Transmission .................................................. 7

1.1.5 Background on Bandwidth Extension .............................................................. 7

1.1.6 Problem Background ..................................................................................... 10

1.1.7 Problem Specification .................................................................................... 11

1.1.8 Motivation and applications ........................................................................... 12

1.2 Contribution ........................................................................................................ 14

1.3 Organization of the Thesis .................................................................................. 14

CHAPTER-2 ........................................................................................................................ 16

Literature Review and Objective of Work ........................................................................... 16

2.1 Literature reviews of different papers .................................................................... 16

2.2 Review sheet for each paper................................................................................. 20

2.3 Summary of Literature Survey ............................................................................... 22

2.4 Definition of the Problem....................................................................................... 23

2.5 Objectives and Overview of the research...............................................................24

CHAPTER-3 ........................................................................................................................ 25

Fundamental of Speech Production Model, Types of Speech coder and its attributes ........ 25

3.1 Introduction ........................................................................................................... 25

3.2 Speech (Voice) sounds..................................................................................................26

3.3 Representation of Speech Signals ......................................................................... 28

3.4 Classification of Coder .......................................................................................... 28

3.4.1 Waveform Coding .......................................................................................... 29

3.4.2 Source Coding ................................................................................................ 30

3.4.3 Hybrid Coding ................................................................................................ 31

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3.5 Speech (voice) coding ........................................................................................... 31

3.6 Issues related to digital speech coding .................................................................. 33

3.7 Speech codec attributes ......................................................................................... 34

3.7.1 Transmission bit rate ........................................................................................... 34

3.7.2 Speech Quality..................................................................................................... 34

3.7.3 Bandwidth............................................................................................................ 35

3.7.4 Communication Delay ......................................................................................... 35

3.7.5 Complexity .......................................................................................................... 35

3.8 Speech codec for next-generation wireless communications................................ 36

3.9 Speech (voice) quality, intelligibility ................................................................... 36

3.9.1 Listening-only tests ............................................................................................. 36

3.9.2 Conversational tests..............................................................................................37

3.9.3 Field tests ............................................................................................................. 38

3.9.4 Intelligibility tests ................................................................................................ 38

3.10 Objective quality evaluation ............................................................................... 38

3.11 Effect of B.W. on speech (voice) quality, intelligibility ..................................... 39

CHAPTER-4 ........................................................................................................................ 40

Development of Bandwidth Extension Model For W.B. To S.W.B. ................................. 40

4.1 Introduction ........................................................................................................... 40

4.2 Non-model based B.W.E. approaches................................................................... 40

4.3 B.W.E. approaches based on the source-filter model ........................................... 41

4.3.1 B.W.E. from N.B. to W.B. Speech Conversion: ................................................. 41

4.3.2 General Model for B.W.E.: ................................................................................. 41

4.3.3 Bandwidth Limitation in Compressed Speech/Audio: ........................................ 42

4.3.4 Baseline System Model for B.W.E...................................................................... 44

4.4 Detailed analysis of B.W.E. based on proposed S.F.M. ....................................... 47

CHAPTER 5 ........................................................................................................................ 59

Linear prediction analysis and synthesis ............................................................................. 59

5.1 Introduction ........................................................................................................... 59

5.2 Speech file Compression based on LPC ............................................................... 59

5.3 Source filter model for sound production ............................................................. 61

5.4 Linear predictive coding (LPC) ............................................................................ 63

5.5 Linear Prediction and Autoregressive Modeling .................................................. 71

5.6 Linear Prediction (L.P.) and Bandwidth extension (B.W.E.) ............................... 72

xvi

5.6.1. LPC analysis ....................................................................................................... 72

5.6.2 LPC estimation .................................................................................................... 72

5.6.3 LPC-synthesis ...................................................................................................... 73

5.7 Bandwidth Extension based on Sub band filter and Evaluation of speech signal

through Source Filter Model ............................................................................................ 75

5.8 Stability consideration (LP) .................................................................................. 80

CHAPTER 6 ........................................................................................................................ 85

Comparative Analysis of Proposed Model with Baseline & Next-Generation Speech Codec

............................................................................................................................................. 85

6.1 Subjective & Objective Measurement for W.B. to S.W.B.................................. 85

6.2 Spectrogram .......................................................................................................... 93

CHAPTER-7 ...................................................................................................................... 100

Conclusion, Major Contribution, and Future Scope .......................................................... 100

Conclusion ..................................................................................................................... 100

Major Contribution ........................................................................................................ 101

Future Scope .................................................................................................................. 101

References .......................................................................................................................... 102

List of Publications ............................................................................................................ 113

xvii

List of Abbreviation

Sr.

No.

Abbreviations Full Form

1

1G

1st generation

2 2G

2nd

generation

3

3G 3rd

generation

4

4G 4th

generation

5

GSM global System for mobile communications

6

3GPP 3rd generation partnership project

7

NB narrowband

8

WB wideband

9 SWB super wideband

10

AMR adaptive multi rate

11

AMR WB adaptive multi-rate wideband

12

BWE bandwidth extension

13 LTE long-term evolution

14

CCITT international telegraph and telephone

consultative committee

15 AMPS advanced mobile telephone system

16 ASR automatic speech recognition

17 PCM pulse code modulation

18 ADPCM adaptive delta pulse code modulation

19 CDMA

code division multiple access

20 CELP code-excited linear prediction

21 PSTN public switched telephone network

22 CCR comparison category rating

xviii

23 WCDMA wideband code division multiple access

24 CS-ACELP conjugate-structure algebraic code-excited

linear prediction

25 EVS enhanced voice services

26 FFT fast Fourier transform

27 VT vocal tract

28 SFM source filter model

29 AMPS advanced mobile telephone system

30 LPC linear predictive coding

31 ITU international telecommunication union

32 ITU-T

telecommunication standardization sector of

the international telecommunication union

33 M.F.C.C. mel frequency cepstral coefficients

34 ISDN integrated services digital network

35 IMT-2000 international mobile telecommunications-

2000

36 GMM gaussian mixture model

37 HB

highband

38 HD high definition

39 HF high frequency

40 LF low frequency

41 DCR degradation category rating

42 EFR enhanced full rate

43 BPF band pass filter

44 KBPS kilo bit per second

45 MBPS megabit per second

46 MOS mean opinion score

47 MDCT modified discrete cosine transform

xix

48 BW bandwidth

49 OLA overlap and add

50 VoIP voice over Internet Protocol

51 LTE long term evaluation

52 UMTS universal mobile telecommunication System

53 VQ vector quantization

54 PESQ perceptual evaluation of speech quality

55 CMOS complementary mean opinion score

56 RPE-LTP regular pulse excitation with long-term

prediction

57 ETSI european telecommunications standards

institute

58 WiMAX 2 worldwide interoperability for microwave

access 2

59 LTE-A long-term evolution advanced

60 VoLTE voice over long term evolution

61 FDMA frequency division multiple access

62 FB full band

63 DRT diagnostic rhyme test

64 MRT modified rhyme test

65 SRT speech reception threshold

66 ENC encoder

67 DEC decoder

68 ABS analysis-by-synthesis

69 HFBE high frequency bandwidth extension

70 SF Spectral folding

71 ST spectral translation

72 NLD non linear distortion

xx

73 LSF line spectral frequencies

74 STFT short-time Fourier transform

75 PSD power spectral density

76 AR autoregressive

77 MOS-LQO mean opinion score linear quality objective

78 TD-CDMA time division code division multiple access

79 SNR signal-to-noise- ratio

80 EB extension band

xxi

List of Symbols

Sr.

No.

Symbol

Description

1 Sn narrowband input signal

2 x feature extracted from narrowband input signal

3 unᶺ estimated narrowband signal from analysis filter

4 uwᶺ estimated wideband signal from synthesis filter

5 ARᶺw Estimate wideband envelope

6 e[n] error signal

7 s[n] AR signal

8 sᶺ[n] predicted AR signal

9 Xwb input wideband signal

10 Xswb estimated super wideband output

xxii

List of Figures

Figure 1.1 A Snapshot of the mobile devices launched in various generations (1G to 4G )....

........................................................................................................................................ 2

Figure 1.2 Average speech spectrum from a 10s long speech sample of a male speaker.. ... 8

Figure 1.3 Bandwidth Extension at the Receiver Terminal................................................... 8

Figure 1.4 Spectra comparison of oiginal, N.B. & W.B. speech signal ................................ 9

Figure 1.5 Step From N.B.-W.B.-S.W.B. telephony ............................................................ 9

Figure 1.6 Missing frequency components of W.B. and N.B. signal. ................................. 11

Figure 3.1 Speech production system model. ...................................................................... 26

Figure 3.2 Representation of speech signals ........................................................................ 29

Figure 3.3 Hierarchy of speech coders ................................................................................ 30

Figure 3.4 General Block Diagram of speech coding .......................................................... 32

Figure 3.5 Voice And Unvoiced speech frame representation ............................................ 33

Figure 4.1 General Model for B.W.E. ................................................................................. 42

Figure 4.2 Bandwidth Limitation in compressed speech/audio ........................................... 43

Figure 4.3 High-Frequency Bandwidth Extension (H.F.B.E.) Algorithm ........................... 44

Figure 4.4 Input and Reconstruction Band .......................................................................... 46

Figure 4.5 Block Diagram of the Proposed approach for W.B. To S.W.B. ......................... 48

Figure 4.6 Pictorial representation for Framing. .................................................................. 49

Figure 4.7 Flow chart representation for Framing .............................................................. 49

Figure 4.8 All Pole spectral shaping synthesis filter ............................................................ 50

Figure 4.9 Linear Prediction (LP) Model of speech creation. ............................................. 51

Figure 4.10 LPC based B.W.E. ............................................................................................ 51

Figure 4.11 Proposed Flow for W.B. to S.W.B. .................................................................. 56

Figure 4.12 Baseline Algorithm Proposed Flow Chart ........................................................ 56

Figure 4.13 Proposed Algorithm Proposed Flow Chart....................................................... 57

Figure 4.14 Data Pre-Processing and Assessment for W.B. to S.W.B................................58

Figure 5.1 LPC co-efficient obtained from Input speech signal .......................................... 59

Figure 5.2 Block Diagram representation of speech file compression based on LPC Analysis

& Synthesis ................................................................................................................... 61

Figure 5.3 Source Filter Model for Sound Production ........................................................ 62

Figure 5.4 Classical Linear Prediction Coefficients (LPC) Model of Speech Production .. 63

Figure 5.5 Linear Prediction as System Identification ......................................................... 65

../../../../RAJ/Desktop/13-9-20/02-10-2020.doc#_Toc52615245


xxiii

Figure 5.6 Prediction-Error Filter ........................................................................................ 65

Figure 5.7 Prediction Gain (PG) as a function of the Prediction Order (M) ....................... 67

Figure 5.8 A Plot of PG Vs Prediction Order (M) for the signal Frames ............................ 68

Figure 5.9 Plots of Prediction Error and Periodograms for the Voiced Frame .................... 69

Figure 5.10 Plots of Prediction Error and Periodograms for the Unvoiced Frame.............. 69

Figure 5.11 Plot of PSD for M=2, M=10,M=20 .................................................................. 70

Figure 5.12 LPC Analysis .................................................................................................... 72

Figure 5.13 LPC Estimation ................................................................................................ 72

Figure 5.14 LPC Synthesis .................................................................................................. 73

Figure 5.15 Input Signal before LPC Analyzer and Input Signal After LPC Synthesizer with

Error Signal................................................................................................................... 73

Figure 5.16 Complete Block Diagram of B.W.E. Output from Band-Limited N.B. Signal 74

Figure 5.17 Bandwidth Extension (B.W.E.) Based on Linear Prediction (LP) ................... 75

Figure 5.18 Bandwidth Extension Based on Sub Band Filter ............................................. 76

Figure 5.19 Performance Evaluation of speech signal Based on Source Filter Model ........ 77

Figure 5.20 Source Filter Model-based Analysis ................................................................ 77

Figure 5.21 Source Filter Model-based Synthesis ............................................................... 77

Figure 5.22 I/P Time-Domain Waveform of Wave File ''Om Shri Ganeshay Namah'' ...... 78

Figure 5.23 O/P Time-Domain Waveform of Wave File "Om Shri Ganeshay Namah" ..... 78

Figure 5.24 Input Frequency Domain Waveform of Wave file "Om Shri Ganeshay Namah"

...................................................................................................................................... 78

Figure 5.25 O/P Freq. Domain Waveform of Wave file "Om Shri Ganeshay Namah" ...... 79

Figure 5.26 Pre-Emphasized speech signal ......................................................................... 79

Figure 5.27 A Hamming Windowed speech signal ............................................................. 79

Figure 5.28 LPC Analyzer Output ....................................................................................... 80

Figure 5.29 LPC Synthesizer Output ................................................................................... 80

Figure 5.30 Auto Cor-Relation co-efficient, Reflection co-efficient & LPC co-efficient ... 81

Figure 5.31 Short-Term Prediction-Error filter connected in series to a Long-Term

Prediction-Error Filter .................................................................................................. 82

Figure 5.32 Block Diagram of the Synthesis filter .............................................................. 82

Figure 6.1 MOS-L.Q.O. For Proposed, H.F.B.E., L.P._Order=12,16,24 & E.V.S. Algorithm

for Bdl_Arctic_A0001.wav .......................................................................................... 88

Figure 6.2 P.E.S.Q. For Proposed, H.F.B.E., L.P._Order=12,16,24 & E.V.S. Algorithm For

Bdl_Arctic_A0001.wav ................................................................................................ 89



xxiv

Figure 6.3 MOS-L.Q.O. For Proposed, HFBE, LP_Order=12,16,24 & EVS Algorithm For

Thirteen Different Speech Files .................................................................................... 90

Figure 6.4 P.E.S.Q. For Proposed, HFBE, LP_Order=12,16,24 & EVS Algorithm For

Thirteen Different Speech Files .................................................................................... 92

Figure 6.5 M.O.S.L.Q. For Proposed, HFBE, LP_Order=24 & EVS Algorithm For Various

Gain For Wave File Bdl_Arctic_A0001.wav ............................................................... 92

Figure 6.6 M.O.S.L.Q. For Proposed, HFBE, LP_Order=16 & EVS Algorithm for Various


Figure 6.7 M.O.S.L.Q. For Proposed, HFBE, LP_Order=12 & EVS Algorithm for Various


Figure 6.8 Spectrogram for Speech File Ma01_01.wav .................................................... 93

Figure 6.9 Spectrogram for Speech File Ma01_02.wav ..................................................... 94

Figure 6.10 Spectrogram for Speech File Ma01_03.wav ................................................... 94



Figure 6.13 Spectrogram for Speech File Fa01_01.wav..................................................... 96




Figure 6.17 Spectrogram for Speech File Arctic-A0002.wav ............................................ 98



Figure 6.20 Spectrogram for Speech File Arctic-A0005.wav ............................................. 99

xxv

List of Tables

Table 3.1 MOS Quality Rating ..................................................................................... 35

Table 3.2 Mean Opinion Score according to ITU-T P.800 ......................................... 37

Table 3.3 Degradation Means Opinion Score according to ITU-T P.800 ................... 37

Table 3.4 Comparison Means Opinion Score according To ITU-T P.800 .................. 37

Table 5.1 AR Synthesizer With ten LPC co-efficient .................................................. 67

Table 6.1 Lister Rating in a Scale of -3 to 3 for different wavefiles.............................78

Table 6.2 MOS-L.Q.O. for Proposed, H.F.B.E., LP_Order=12,16,24 & E.V.S. algorithm

for bdl_arctic_a0001.wav ............................................................................................ 88

Table 6.3 P.E.S.Q. For Proposed, H.F.B.E., LP_Order=12,16,24 & E.V.S. algorithm for

bdl_arctic_a0001.wav ................................................................................................... 89

Table 6.4 MOS-L.Q.O. for Proposed, H.F.B.E., LP_Order=12,16,24 & E.V.S. algorithm

for Thirteen different speech files ................................................................................ 89

Table 6.5 P.E.S.Q. for Proposed, H.F.B.E., LP_Order=12,16,24 & E.V.S. algorithm for

Thirteen different speech files ...................................................................................... 90

Table 6.6 M.O.S.L.Q. for Proposed, H.F.B.E., LP_Order=24 & E.V.S. algorithm for

various Gain for wave file bdl_arctic_a0001.wav ........................................................ 91

Table 6.7 M.O.S.L.Q. For Proposed, H.F.B.E., LP_Order=16 & E.V.S. algorithm For


Table 6.8 M.O.S.L.Q. For Proposed, H.F.B.E., LP_Order=12 & E.V.S. algorithm For


Fruition of Communication Systems

1

CHAPTER-1

Introduction

1.1 Fruition of Communication Systems

As the imperative of the remote transmission recurrence band assets due to the

complexity of the 3G versatile communications environment, reproduced speech (voice)

quality, comprehensible at the recipient side is found hardened, scarcely capable of being

heard, thin since of the constrained transmission capacity of 300-3.4KHz. In contrast,

Today's terminals, infrastructure, operate at wider bandwidths for which speech (voice)

quality, intelligibility are greatly improved. Normally, the total move from narrowband (300-

3.4KHz) to wideband (0.05-7kHz) and wideband (0.05-7kHz) to super-wideband (0.05-

14kHz) communications will require impressive time. As a result, wideband, super-

wideband innovation must interoperate with narrowband innovation. In this case, clients will

involvement significant varieties in speech (voice) quality and intelligibility. This chapter

discusses brief highlight of fruition of communication system followed by contribution,

organization of thesis.

1.1.1 Analog , Digital telephony

The limited N.B. speech (voice) signals, were sent out over different frequency

channels with a frequency separation of 4kHz. The first wireless speech (voice) transmission

started in year 1915 [1]. In year 1937, Alex Reeve has visualized time-division multiplexing

based pulse code modulation (PCM) which has shaded light for voice communication

digitization [2]. The commercial utilization of PCM was started in the year 1950s [3]. By the

existing PSTNs, PCM adopted the typical N.B. B.W. for communication. So, for long period

of times, subscribers were offered only N.B. communication services.

1.1.2 Wireless Cellular Networks

After Marconi's doing well attempt at wireless transmission, engineers, academicians,

scientists started to carry out the research and development ( R & D) activity for R.F. based

proficient communication [1]. The 1st

generation (1G) wireless mobile phone system was

developed in 1973, but not commercialized until year 1984. Wireless communications have

Introduction

2

remarkably gone ahead in last few years. Mobile handsets have also advanced together with

the generations (from 1G to 4G) with added functionalities.

Fig. 1.1 depicts a snapshot of the mobile devices launched in various generations (1G

to 4G). The first generation cellular systems, introduced in the year 1980s, used analog

cellular, cordless telephone technology [2]. Second-generation (2G) systems, arrived in year

1980s, used digital speech (voice) transmission. 2G services played a vital role for voice

transmission which was inefficient for data transmission [4].Third-generation (3G) systems

entered in market in the year 2000. It provides advanced voice, high-speed data services in

comparisons with Second-generation (2G) systems. It utilized packet switching for data

transmission while circuit-switching for voice transmission. Universal Mobile Tele-

communication System (UMTS) or wideband CDMA (WCDMA), time division-

synchronous CDMA (TD-SCDMA), CDMA2000 are the most popular 3G technologies [4].

Packet switching technology employed in fourth generation (4G) systems supports 100Mbps

data rate. It is used in system for voice as well as data services. WiMAX 2, LTE-Advanced

are the two most popular 4G protocols [4]. Third as well as fourth generation wireless

communication (W.C.) system are becoming popular in the world market due to their low bit

rate coder. It was proposed to provide interactive multimedia communication including

teleconferencing, internet access an combination of another benefit that gets to be practicable

with low complex, low cost, low bit rate, less processing delay. So one can articulate that

these are the major attributes that play a vital role while designing any particular coder.

Figure 1.1 A snapshot of the mobile devices launched in various generations [5]


3

1.1.3 Speech Coding

Speech coding is defined as the procedure of shrinking the bit rate of digital speech

(voice) signal representation for communication or storage while maintaining a speech

(voice) quality adequate for the application [6]. In general, it is the way to represent the

speech (voice) signal with the fewest number of bits, while maintaining a sufficient level of

quality of the retrieved or synthesized speech with reasonable computational complexity. To

achieve high-quality speech at a low bit rate, one can apply coding algorithm (a sophisticated

method to reduce the redundancies from the speech signal).

Although wide-bandwidth channels, networks are becoming more viable, speech

(voice) coding for bit-rate reduction has retained its importance due to the need for low bit

rates for cellular, Internet communications over constrained-bandwidth channels, voice

storage systems. Due to the increasing demand for digital speech communication, speech

coding technology has received augmenting levels of interest from the research and

standardization. Speech coding standards have played, and continued to play an important

role in the development, use of speech codes. There are a few speech coding applications in

which inter-operatability is not an issue. An example of such an application is a digital

answering machine or digital voice mail system in which the same system is used to both

encode, decode the speech. For such applications, the speech coder of choice can be the best,

most cost-effective available at the time when the system is designed, without regard to

inter-operatability. For the vast majority of applications, however, inter-operatability is a

major issue. All telecommunications applications belong to this class. For inter-operatability

to be achieved, standards must be defined as well as implemented. This encourages the

research community to investigate alternative techniques for speech (voice) coding with the

objectives of overcoming deficiencies. The standardization community pursues the

establishment of standard speech coding methods for various applications that will be

widely accepted, implemented by the industry. A variety of standards bodies have been

responsible for the definition of new standards. Speech (voice) coding can be categorized

based on equipped bandwidth.

N.B. coding:

N.B. coding techniques squeeze speech (voice) signals in the range of 0.3-3.4kHz [7].

Introduction

4

Pulse-code modulation (PCM):

PCM is a waveform coding technique that carries out discrete-time, magnitude

approximation of continuous signals in the time field [8]. ITU- recommendation G.711 [9],

G.712 [10] normalized the transmission characteristics of PCM for speech (voice) signals.

Parametric coders (L.P.C.) code a set of model parameters in place of the time domain

waveforms. To take benefit of both the schemes, the combination of both the coding method,

hybrid method can be utilized. In this method co-efficient of synthesis filter are sent as a side

information while quantization of L.P. residual error (difference between actual and

predicted sample difference) is done via CELP coding [11].

For economical, complexity reasons, bit rate reduction from 64kbps is a most

important requirement [11]. ITU-T- recommendation G.726 [12] homogenized an PCM

extension known as ADPCM [12]. ADPCM supports multiple bit rates of 16, 24, 32 &

40kbps. In ADPCM quantization of error signal is carried out [13].

GSM full-rate (GSM-FR), enhanced full-rate (EFR) codec:

GSM full rate (GSM-FR) codec relies on LPC has employed short-term LP analysis

for spectral envelope modeling, long-term LP analysis to attain a residual error signal. The

Quantization of attained signal is done through ADPCM which operates at 13kbps was

adopted by ETSI-recommendation GSM 06.10 [14]. In year 1992, GSM-FR was further

improved using a well-organized V.Q. system for residual error signal using ACELP

algorithm [15-16]. It was normalized as GSM-EFR codec in ETSI- recommendation GSM

06.60 [17] in 1996, operates at 12.2kbps. The GSM-EFR codec achieved speech (voice)

quality comparable with ADPCM at 32kbps [11].

G.729:

It is normalized in ITU-T-recommendation G.729 [18] that in the year 1995, it was

operated at 8kbps. The codec was based on conjugate-structure ACELP which was widely

employed in VOIP infrastructures.

Adaptive multi-rate (AMR):

An expansion of the EFR codec with eight possible bit rates ranging from 4.75 to

12.2 kbps, was standardized by ETSI Recommendation GSM 06.90 for 2G & 3G system

[19]. 3GPP takes up AMR as the default speech (voice) codec for 3G wideband systems


5

(UMTS, CDMA2000) as mentioned in 3GPP TS 26.090 [20]. AMR coding occupies the

transcoding of AMR-coded speech (voice) signals to/from PCM format [21].

Wideband coding:

As W.B. transmission improves the quality of voice transmission, there is a rising

demand for W.B. communication services in fixed, mobile networks at lower bit rates. The

first W.B. speech codec (voice) for ISDN, tele-conferencing was homogenized in year 1985

by CCITT [11]. The ITU-T recommendation G.722 [22] spell out the characteristics of an

audio W.B. coding system in which a frequency band is split into two, lower and higher,

sub-bands in a way that both are encoded using sub band ADPCM. The G.722 standard is

employed as a benchmark for other codec assessment [13]. In the year 1999, a low-

complexity W.B. codec was commenced in ITU-T recommendation G.722.1 [23]. Low-

complexity W.B. codec attained as good as speech (voice) quality at reduced bit rates of

24kbps, 32 kbps.

Adaptive multi-rate wideband (AMRWB):

AMRWB encodes speech (voice) within a bandwidth of 0.05-7kHz. The AMRWB

codec relies on the ACELP technique was first standardized in the year 2001 by 3GPP TS

26.190 [24] for 3G systems. It utilizes B.W.E. for signal re-synthesis beyond 6.4kHz as

specified in ITU-T recommendation G.722.2 [25]. It supports nine-bit rates ranging from 6.6

to 23.85 kbps but achieves higher speech quality at 8.85 kbps in comparisons with AMR at

12.2kbps [26]. Speech (voice) transmission by AMRWB provides significantly better quality

than N.B. due to B.W. expansion results in much more natural sound. By May 2016, 164

mobile operators (17 on GSM (2G), 130 on UMTS (3G), 63 on LTE (4G) networks) have

commenced commercial HD voice services in 88 countries [21].

G.729.1:

ITU-T Recommendation G.729.1 [27] sanctify an extension to the G.729 codec

provide scalable N.B.,W.B. coding of speech (voice), audio signals from 8-32kbps [28].

S.W.B. or F.B. coding:

S.W.B. or F.B. speech (voice) communications send out almost complete human

speech (voice) spectrum results in much more natural, understandable speech (voice) sound

Introduction

6

in comparisons with N.B. or W.B. communications..

G.729.1 Annex E:

G.729.1 Annex E [29] broadens the 32kbps mode of the G.729 codec to S.W.B. mode

providing bit rates in the range of 36-64kbps. An S.W.B. extension to the scalable W.B.

codec G.729.1 proposed in [30] achieve improved audio quality in comparisons with

existing S.W.B. extension G.722.1 Annex E.

Extended AMRWB (AMRWB+):

The AMRWB standard (3GPP TS 26.290 [31]) is a S.W.B.E. to the AMRWB codec

operates up to an augmented frequency range of 16kHz, bit rates up to 32kbps [14]. It is a

mixture of two codec (hybrid codec) that combines linear predictive, transform coding

techniques depending on the signal type, e.g., speech (voice) or audio [32].

G.719:

A low-complexity coding algorithm for F.B. speech (voice), audio signals is

described in ITU-T recommendation G.719. The coding technique offers bit rate from 32 to

128 kbps. [33]

HE-AAC:

The high-efficiency advanced audio codec (HE-AAC) makes use of spectral band

replication (SBR) approach for efficient coding of audio signals [34-36]

Over the top (O.T.T.) conversational codec:

O.T.T. service providers (Skype) give point-to-point services intended for voice over

internet protocol. The utilization of SILK codec allows, conventional N.B. speech (voice)

services to be transferred towards W.B., S.W.B. communications via the use of broadband IP

services [37]. OPUS is another high-quality codec which carry out hybrid coding,. OPUS

involves the coding of frequency up to 8kHz using the SILK codec, while above 8kHz

frequency are coded using CELT [39]. It gives S.W.B. or F.B. transmission at, above 24

kbps[37].


7

Enhanced voice services (EVS) codec:

3rd

generation partnership project (3GPP) has carried out a study in the year 2010

regarding EVS codec [38]. The homogenization of it was done in year 2014. EVS codec can

encode a speech (voice) as well as other audio signals with an S.W.B. (0.05-14kHz) at bit

rates as low as 9.6kbps [28]. It operates at four different B.W. (i.e. N.B. (0.02-4kHz), W.B.

(0.05-7kHz), S.W.B. (0.05-16kHz), F.B. (0.05-20kHz) [37]). EVS supports twelve bit rates

ranging from 5.9 to 128 kbps with S.W.B., F.B. services starting at or above 9.6, 16.4kbps

correspondingly [48]. Subjective listening tests show that EVS outperforms in comparisons

with all existing conversational voice, audio codec across all bit rates, B.W. [39]. Detailed

technical details of EVS can be found in [40-43]. In upcoming scenario of advancement in

next generation wireless communication system due to the key features provided by the EVS

codec, mobile operators have started enabling their networks for EVS support [44].

1.1.4 Background on Digital Speech Transmission

In digital signal processing (DSP), signals are band-limited w.r.t. use of sampling

frequency. In analog telephone speech, B.W. is limited to 300-3.4 kHz. Due to our human

hearing system's ability to detect fundamental frequency based on the harmonics present in

the signal, the researchers, academicians have always targeted towards high frequency

compared to low frequency. Also from a speech quality & intelligibility point of view, high-

frequency content is much more significant than low frequency.

Fig.1.2 depicts the average speech (voice) spectrum deliberate from a 10s long

speech (voice) sample of a male speaker [45]. From the diagram one can say that amount of

information in N.B. is small compare to W.B. & S.W.B. In W.B amount of information is

more compare to N.B. & less compare to S.W.B. By investigating at various levels, the

researcher has found that B.W. limitation of transmitted speech (voice) resulting in much

inferior quality compares to face to face conversion due to diminution in speech (voice)

quality & intelligibility.

1.1.5 Background on Bandwidth Extension

In the recent scenario of advancement in the next-generation wireless communication

systems, due to missing high band (H.B.) component information, limitation of N.B. to

represent consonant sound speech (voice) signal looks like stiffened, barely audible & slim.

Introduction

8

Figure 1.2 Average speech spectrum deliberate from a 10s long speech (voice) sample of a

male speaker. The N.B. bandwidth (300-3400 Hz) is colored red, W.B. bandwidth (50-7000

Hz) is colored blue, super wideband (S.W.B.) (50-14000 Hz) yellow [45]

Fig.1.3 illustrates the bandwidth extension at the receiver terminal. As depicted in

Fig. 1.4 N.B. speech may be short of spectrum compared to W.B. The difference between all

three spectrum are quite obvious, which may result in speech (voice) quality degradation.

A general block diagram of N.B.-W.B.-S.W.B.telephony is illustrated in Fig. 1.5. As

represented in fig. 1.5(a) B.W.E. happens at the receiver side while in figure 1.5(b) it

happens at the network side. In fig. 1.5(c) the information required to acquire missing H.B.

component from band-limited N.B. speech is sent via sideband information or with N.B..

signal itself via digital watermarking. As depicted in fig.1.5(d) direct W.B. to W.B.

transmission from the far end to the near-end terminal. As depicted in fig. 1.5(e), B.W.E. can

be done on the receiver side to get S.W.B. from the W.B. signal.

Figure 1.3 Bandwidth extension at the receiver terminal [46]


9

Figure 1.4 Spectra comparison of original, N.B. & W.B. speech (voice) signal [46]

Figure 1.5 Step from N.B.-W.B.-S.W.B. telephony [46]

For the reconstruction of H.B.components B.W.E. can be categorized into two types namely

blind and non-blind schemes.

Introduction

10

Non-blind methods:

Non-blind B.W.E. scheme recuperate missing H.F. components at the receiving-end

from auxiliary side information related to H.F. that are encoded into a data stream together

with N.B. components [27]. The inclusion of such side information incurs an additional

burden of 1-5kbps [47]. Examples are enhanced AMR codec [48], HE-AAC codec [34-35],

AMRWB codec [20].

Blind methods:

Blind B.W.E. methods estimate missing H.B. components, using only the available

N.B. components. Such B.W.E. solutions thus exploit the correlation between N.B. and H.B.

components of speech (voice), estimated missing H.B. components using a regression model

is learned from W.B. speech training data.

1.1.6 Problem Background

Within the present day state of undertakings, non wired, wired communication

structure encompassed by various substantial basis speech (voice) quality is in general

corrupted at the recipient side. One of the fundamental and most vital sound chosen & well-

thought-out is, the arrangement of N.B. supporting B.W. of 300 Hz-3.4kHz. The drawback

of the N.B. communication structure is that the speech (voice) signal is sounds stifled, thin

due to the non-attendance of High Band (H.B.) spectral components [49]. The limited

frequency band trims down superiority and clearness of speech (voice) signal. As a result of

going off track high-frequency components which play a noteworthy part especially in

consonant sounds[7].

Due to missing high band (H.B.) components, limitation of N.B. to represent

consonant sound like a ship, pin, run, work, etc.. have resulted in speech signal that looks

like stiffened, muffled, and thin [45]. To get better quality, clearness, naturalness,

pleasantness, the brightness of speech (voice) signal N.B. must be upgraded to the W.B.

system. The utilization of the W.B. system makes it is necessary to upgrade the transmission

network, terminal devices to W.B.. Software cum hardware up-gradation, compatibility,

feasibility problem need to be solved for abruptly substitute the entire existing N.B. coding

system.[46].


11

1.1.7 Problem Specification

Fig.1.6 depicts the original W.B. and down sampled N.B. speech (voice) signal with

a spectrogram. A closer look at fig.1.6 reveals that N.B. speech may lack significant parts of

the spectrum and the difference between the original W.B. and down sampled N.B. speech

(voice) signal is still noticeable. So, creating the missing frequency components 3.4 kHz to

4.6 kHz will be an exigent task [50].

Figure 1.6 Missing frequency components of W.B. and N.B. signal [50]

Bandwidth extension techniques of speech (voice) are mostly categorized into two

classes. In first, the absent frequency element is attained from the available narrowband

voice element regardless of information transmission about the stolen frequencies [5]. The

second relies on information hide process. The prior strategy makes W.B. speech (voice)

signal by source filter model (S.F.M.). It expresses the excitation signal and LPC coefficient

for spectral envelope [51]. In the second method, W.B. line spectral pairs (LSP) bits can be

put in a watermark in N.B. PCM samples; In simple words B.W.E. techniques w.r.t.

steganography methods can insert high-frequency components onto N.B. speech (voice) data

or bit-stream. A Limitation of the B.W.E. method with steganography is that analogous

Introduction

12

techniques require to be bolstered at two ends of the transmission line [7,52]. The work

employed in this thesis will address these issues by doing a B.W.E. of speech (voice) signal

based on a source-filter model (S.F.M.), perform subjective and objective measurements on

the MATLAB platform.

In the recent scenario of advancement in next generation wireless technology, several

smart devices support high-quality speech (voice) communication services at S.W.B. but it is

fact that speech (voice) quality is corrupted when they are utilized with devices or network,

be deficient in S.W.B. support. So either B.W.E. or suitable S.W.B. codec is a primary

requirement for next-generation wireless communication. To Propose a highly efficient

algorithm with tiny latency is a major focused area of researchers for next-generation

wireless communication.

1.1.8 Motivation and applications

This segment lists out the applications of B.W.E. in different state of affairs.

When network and/or mobile terminals do not support W.B. communication:

To get better speech quality offered by traditional telephony infrastructure, coding

techniques have been developed to squeeze information at higher B.W.. Calls at higher B.W.

are feasible only if the complete communication path supports operations at the identical

B.W. Lack of either leads to a reduction in B.W and thereby a reduction in speech quality.

In the recent scenario, a combination or interconnection of different networks, mobile

devices supports N.B.,W.B.,S.W.B. communications [53]. While the exploitation of W.B.

codec, networks is in progress, it is slow as it incurs costs to the network operators as well as

end-users. Additionally, a phone call may involve a landline device that restricts in the B.W.

to N.B. by default. So, even today, a significant portion of calls operate in N.B. mode

whereas the migration to W.B. will take considerable time [54]. N.B., W.B. networks,

devices (or terminals) will thus coexist for some upcoming years, leading to mobile phone

calls of different B.W. at the receiving terminal [55].

When a W.B.-to-N.B. handover occurs during a phone call:

Due to the attendance of heterogeneous communication networks [56], the process of

B.W. switching from W.B. to N.B. may take place for the period of an ongoing phone call.

This may take place either due to handovers between two different networks or due to

diminishes in network resources that cause dynamic fall back from W.B. to N.B. mode [57-


13

58]. This may lead to sudden changes in quality, so the irritating client involvement.

According to [55], a W.B. to N.B. handover leads to perceived speech (voice) quality even

below N.B. A possible solution to avoid this problem is to switch to W.B. communication as

soon as the network resources are enlarged. However, subjective tests have indicated that

switching from N.B. to W.B. speech (voice) is perceived as an impairment unless the

transition happens early enough or at the beginning of the call [58]. According to [57], W.B.

transmission should continue at least for 30 seconds after switching to benefit from improved

speech (voice) quality. So, instead of switching from N.B. to W.B. B.W.E. can be used as

soon as the call falls back to N.B. mode, thereby mitigating the need for a true W.B. call. A

comparison between the subjective quality of two switching schemes, namely transitions

between W.B., N.B. (AMR coded) speech (voice) and transitions between W.B. ,bandwidth-

extended N.B. (AMR coded) speech is reported in 3GPP TR 26.976 [59].

When there is a B.W. mismatch stuck between training, testing data:

For definite applications i.e..speaker recognition, great amounts of N.B is available

for model training. To operate upon N.B. data, the conventional approach is to perform down

sampling from W.B. to N.B. However, this leads to the thrashing of helpful spectral content

in W.B. speech. B.W.E. can therefore be utilized to shrink the B.W. mismatch between

speech data recorded at different sampling rates. N.B. speech data can be B.W. extended and

then be used with an available W.B. data. This is supportive in two aspects. First, the amount

of W.B. training data can be augmented for the training of W.B. models. Second, already

trained W.B. models can still be used (with the application of B.W.E.) in the case that test

data is N.B.; re-training of the models with N.B. data is no longer necessitated. The use of

B.W.E. therefore permits for only one model to be trained while still underneath different

B.W. modes. B.W.E. has been investigated for applications such as speaker recognition &

automatic [60,61-63], speaker identification & verification [64-65] to perk up the

performance of W.B. models by increasing the amount of W.B. training data through B.W.E.

When clients have hearing impairments:

People with impaired hearing use of hearing aids frequently face difficulties during

telephone calls due to bandwidth limitations. Bandwidth extension can be used to improve

the intelligibility, quality of N.B. speech for such users [66]. The study reported in [67]

showed that users with hearing impairments can tolerate overestimated energies in the B.W.,

speech (voice) sounds, thereby providing increased intelligibility.

Introduction

14

Super-wide bandwidth extension

With the progress in S.W.B. or F.B. speech coding techniques, many smart devices

and networks now support high-quality speech communication services at super-wide

bandwidths. However, in today's heterogeneous networks, S.W.B. devices are repeatedly

bring into play with other devices, networks which support only N.B. or W.B.

communications. While they generally put forward backward compatibility, users of S.W.B.

devices will then be restricted to N.B. or W.B. communications.. S.W.B.E. has the objective

of humanizing the gap in quality between W.B., S.W.B. communications.

1.2 Contribution

The steps followed in this research progression are

To do the literature survey relating to bandwidth extension (B.W.E.) algorithms,

define problem and set the objective.

Study the speech production model, speech transmission, quality, intelligibility.

Learn the source-filter model(S.F.M.) based on linear prediction analysis.

Study digital Analyzing the existing speech bandwidth extension methods for W.B.

and S.W.B. with its pros and cons.

Develop a bandwidth extension model for W.B. to S.W.B. conversion by taking a

general model for B.W.E. from N.B. to W.B. as a reference and utilizing the concept

of baseline model based on high-frequency bandwidth extension(H.F.B.E.).

Perform MATLAB based simulation for subjective as well as an objective

measurement of the proposed model and compare the results with baseline as well as

next-generation speech codec.

1.3 Organization of the Thesis

Chapter 1 presented the fruition of communication systems. problem, scope, outline of the

contributions of the research.

Chapter 2 is about literature survey relating to bandwidth extension algorithms (B.W.E.),

define problem and set the objective.

Chapter 3 discusses about speech production model, digital speech transmission, speech

quality & intelligibility, speech coding basics, classification of coder, speech codec attributes

etc..

Organization of the Thesis

15

Chapter 4 talks about the development of the B.W.E. model for W.B. to S.W.B. conversion

by taking detailed analysis of N.B. to W.B. model and reference Algorithm.

Chapter 5 analyses about linear prediction analysis, synthesis with stability criteria.

Chapter 6 discusses MATLAB based simulation for subjective as well as an objective

measurement of the proposed model and compare the results with baseline as well as next-

generation speech codec.

Chapter 7 concluded with overall results and highlights the scope for future work in this

area of research.

Chapter 8 presents a summary of the list of publications and references.

Literature Review and Objective of Work

16

CHAPTER-2


2.1 Literature reviews of different papers

Review of Paper 1

Title of Paper

Evaluation of Levinson-Durbin Recursion Method for Source-

Filter Model-based Bandwidth Extension Systems

Author

G.Gandhimathi, Dr. A. Hemalatha, Dr.S.P.K.Babu

Publication Year

2015

Publication

International Journal of Scientific & Engineering Research,

Periyar Maniammai University Thanjavur, India

Review:

Bandwidth extension (B.W.E.) techniques are employed to generate a W.B. signal

from the N.B. signal. Since most of the H.F. components, fricative consonants were absent in

the N.B. representation of the sound, it is a challenging task to create those missing

components in the W.B. equivalent signal. In this paper, the author has evaluated the

performance of two autoregressive (AR) modeling methods, autocorrelation method (LPC),

Levinson-Durbin recursion method. These estimation methods lead to approximately the

same results (same coefficients) for particular autoregressive parameters, but the small

differences in such estimations will have a great impact on the quality of reproduced sound.

The author has implemented a source-filter model-based speech bandwidth extension system

with the above two AR modeling methods, validated their performance with suitable

metrics..

Literature reviews of different papers

17

Review of Paper 2

Title of Paper

Bandwidth Extension for Speech in the 3GPP EVS Codec

Author

Venkatraman Atti ,Venkatesh Krishnan, Duminda Dewasurendra,

Venkata Chebiyyam

Publication Year

2015

Publication

IEEE International Conference on Acoustics, Speech and Signal

Processing (ICASSP)

Review:

In this paper, the author has approached the time-domain bandwidth extension

(T.B.E.) structure employed to code W.B., S.W.B. speech in the newly standardized 3GPP

enhanced voice service codec (E.V.S.). In particular, the advanced modeling techniques used

to recreate the W.B., S.W.B. frequencies with a fewer number of bits paved the way for EVS

to become the most advanced feature-rich conversational speech coder of its time. At 13.2

kbps, the S.W.B. coding of speech uses as low as 1.55 kbps for encoding the spectral content

from 6.4-14.4 kHz. Extensive MOS testing as per ITU-T P.800 has proven that the EVS

codec with time-domain bandwidth extension outperforms in comparisons with all other

standard codec references with significant margins, making it the ideal codec to be deployed

in modern VoLTE networks and other VOIP networks such as VoWiFi.

Review of Paper 3

Title of Paper

Simulation and overall comparative evaluation of performance

between different techniques for high band feature extraction based

on bandwidth extension

Author

Ninad S. Bhatt

Publication Year

2016

Publication

Springer, Int. Journal of Speech Technology

https://ieeexplore.ieee.org/xpl/conhome/7158221/proceeding

https://ieeexplore.ieee.org/xpl/conhome/7158221/proceeding


18

Review:

In this paper the author inspect, study, simulate calculation of the High band (H.B.)

component based on linear predictive coding (L.P.C.), M.F.C.C. techniques. The author has

made comparisons of B.W.E. using LPC, MFCC technique for different extension of

excitation. From the simulation result, bar chart for mean opinion score (MOS), Perceptual

evaluation of speech quality (P.E.S.Q.) highlighted by the author in the paper it is clear that

amongst various excitation methods the results obtained for sinusoidal transform

coding(S.T.C.) [82], Full-wave rectification (F.W.R.) as a Non-linear Distortion (N.L.D.)

component produce good results in comparisons with all other techniques for various speech

files for LPC, MFCC based methods[26].

Review of Paper 4

Title of Paper

Audio Bandwidth Extension

Author

Somesh Ganesh

Publication Year

2016

Publication

Georgia Institute of Technology Atlanta, Georgia

Review:

In this paper, the author has provided evidence that the audio bandwidth extension is

a technique used to improve the perceived quality of band-limited audio generated using

various audio codecs. The author proposes a few methods for audio bandwidth extension and

evaluates them by performing listening tests. A comparison between half-wave rectification,

full-wave rectification, along with the use of sub-band filtering is done. The wrapping up

from the experiment conducted is that half-wave rectification performs better than full-wave

rectification as a non-linear device and use of sub-band filtering improves the amount of the

perceived quality in the acquired bandwidth extended output signal.


19

Review of Paper 5

Title of paper B.W.E. of Speech Signals using Quadrature Mirror Filter

Author

Janki Patel, Nikunj V. Tahilramani and N.S.Bhatt

Publication Year

2018

Publication

IEEE- International Conference on Computation of Power,

Energy, Information and Communication (ICCPEIC)

Review:

In ordinary telephone networks, the speech signal looks like stiffened, muffled, thin

due to limited N.B. frequency range (300 Hz to 3.4 kHz). So it is a prime requirement to

apply the bandwidth extension algorithm on it. Here QMF based B.W.E. system is proposed

which analyzes, synthesizes the speech (voice) signal to acquire W.B. speech (voice) signal

at the receiver side to improve the quality and naturalness of the speech (voice). By applying

the B.W.E. algorithm, the missing higher frequency components of speech can be added at

the end terminal to produce W.B. speech. The objective, subjective evaluations show that the

quality of speech synthesized by the proposed method is better than N.B. speech.

Review of Paper 6

Title of Paper

Super Wideband Spectral Envelope Modeling for Speech Coding

Author

Guillaume Fuchs, Chamran Ashour

Publication Year

2019

Publication

INTERSPEECH 2019, September 15–19, 2019, Graz, Austria,

Erlangen, Germany,

Review:

N.B. speech traditionally transmitted in communication, has been extended to W.B.

(W.B. up to 8 kHz) in digital communications by different standards [83-85]. This trend

continued with the more recent introduction of super-wideband (S.W.B. up to 16 kHz)

speech coders [86-87]. Nonetheless, existing S.W.B. coding solutions are all built on a dual-

band system, coding the lower band separately from the upper band according to a source-


20

filter model of speech production, pure perceptual considerations using a parametric

representation respectively. The spectral envelope is generally modeled in speech coding by

Linear Predictive Coding (LPC) parameters characterizing the vocal tract and acting as an

autoregressive estimation of the speech. Although applying LPC to N.B. and W.B. speech

(voice) is well studied in the literature [88], very little consideration has been given in the

past to the S.W.B. The widening of the audio B.W. has two major impacts on the design of

LPC; the order of the prediction, design of the high-frequency pre-emphasis filter usually

applies before the linear prediction analysis. As the number of samples increases with a

higher sampling frequency, the prediction order must be adjusted accordingly. The LPC

order of 10 usually adopted for N.B. was increased to 16 for W.B. and should probably be

even higher for S.W.B.. However, the order has a significant impact on the bit-rate required

for transmitting the LPC coefficients.

2.2 Review sheet for each paper

Table 2.1 Review summary of all research paper.

Sr.

No

Paper Title

Review:

comment

(include work was done)

1 Evaluation of Levinson-

Durbin Recursion Method

for Source-Filter Model-

based Bandwidth Extension

Systems

1. Source-filter model-based B.W.E. using LPC

coefficients is a simple, reliable technique to achieve

B.W.E. from N.B. to W.B. and it can make a pedestal

for B.W.E. from W.B. to S.W.B.

2. From the Spectrogram comparison of N.B.,

Extended W.B. output by B.W.E., one can note that the

proposed algorithm can able to represent consonant

sounds, mostly fricative consonants like ( th /, sh/, / Isl

/, xl/ ch, /, etc..).

2 Bandwidth Extension for

Speech in the 3GPP EVS

Codec

1. At 13.2 kbps, the super-wideband coding of speech

(voice) uses as low as 1.55 kbps for encoding the

spectral content from 6.4-14.4 kHz.

2. Subjective (MOS) testing as per ITU-T P.800 has


21

confirmed that the EVS codec with time-domain

B.W.E. performs well in comparison with all other

standard codec references, making it the ideal codec to

be deployed in modern VoLTE & VOIP network.

3

Simulation and overall

comparative evaluation of

performance between

different techniques for high

band feature extraction

based on bandwidth

extension

1. The method employed in this paper is a narrative

approach to inspect, study, simulate calculation of the

High band (H.B.) component based on linear predictive

coding(L.P.C.), Mel frequency cepstral coefficient

techniques.

2. Amongst various excitation methods the results

obtained for sinusoidal transform coding(S.T.C.), Full-

wave rectification (F.W.R.) as a Nonlinear Distortion

component produce good results in comparisons with

all other techniques in terms MOS score for various

speech files for LPC and MFCC based methods.

4 Audio Bandwidth Extension 1. Audio bandwidth extension is a technique used to

improve the perceived quality of band-limited audio

generated using various audio codec.

2. From the conducted listening test as per ITU-T

recommendation for various methods of bandwidth

extension, one can make a note that half-wave

rectification performs better than full-wave

rectification as a non-linear device. The use of sub-

band filtering improves the amount of the perceived

quality in the bandwidth extended signal.

5 Bandwidth Extension of

Speech Signals using

Quadrature Mirror Filter

1. QMF based B.W.E. system analyzes and synthesizes

the speech (voice) signal to acquire W.B. speech

(voice) signal at the receiver side to improve the

quality, naturalness of the speech (voice).

2.The missing higher frequency components of speech

(voice) can be added at the end terminal to produce

W.B. speech (voice).

3.The objective, subjective evaluations show that the


22

quality of speech (voice) synthesized by the proposed

method is better than N.B. speech.

6 Super Wideband Spectral

Envelope Modeling for

Speech Coding

1.The author is paying attention to the spectral

envelope modeling by Linear Predictive Coding (LPC)

parameters characterizing the vocal tract.

2. The order of the prediction, design of the high-

frequency pre-emphasis filter usually applied before

the linear prediction analysis.

3. As the number of samples increases with a higher

sampling frequency, the prediction order must be

adjusted accordingly. The LPC order of 10 usually

adopted for N.B. was increased to 16 for W.B. and

should probably be even higher for SW.B.

4. The order has a significant impact on the bit-rate

required for transmitting the LPC coefficients.

2.3 Summary of Literature Survey

Bandwidth extension algorithms (B.W.E.) aspire to estimate missing higher

frequency components at 3.4-8kHz for W.B. or 6.8-16kHz for S.W.B. signal. In this chapter,

a survey of approaches to B.W.E. is presented. While B.W.E. approaches focus on N.B. to

W.B. extension, S.W.B.E. approaches for W.B. to S.W.B. extension are developed to bridge

the quality gap between W.B., S.W.B. communication. While studying bandwidth extension

several parameters, constraints need to be taken into consideration to propose a highly

efficient algorithm that introduces only negligible latency. During the literature review many

research papers, websites, journals, other articles on wireless-communication [1-5], [10],

ETSI standard [14],[17],[19], ITU-T Recommendation [8],[9],[12],[18],[22-23]

[25],[27],[29],[33],[109],[137-138],[140],[145], 3GPP TS series [20],[38],[40],[41],[59] for

coding of speech followed by B.W.E. [6],[34],[35],[54],[55],[62],[64],[65],[67],[77] are

referred. A general overview of the bandwidth extension has been presented in many

research papers [7],[13],[45-46],[50-52],[68][81]. This step led us to define the research

problem. In [69] author has given a solution to trim down the wideband speech coder bit-rate

Summary of Literature Survey

23

by coding the parameters of wideband voice (speech) employing noteworthy enlarge in bit-

rates of N.B. coders. [51-52],[70-71] have discussed various approaches (i.e..Linear

Prediction algorithm using L.P.C. coefficients, codebook mapping approach) and make

subjective measurement comparison for various audio wave files.

Performance assessment of speech (voice) signal based on the sub-band filter

followed by source-filter model are discussed in [72]. Based on review it is found that Linear

prediction is an effective tool for the bandwidth extension. It plays a very significant role in

achieving data rate compression. After studying detailed linear prediction analysis, synthesis,

stability criteria, it is found that for next-generation wireless communication bandwidth

extension from the signal itself (without sending sideband information) is useful for data rate

reduction because no need of sending information in sideband is needed for reproduction of

signal at the receiver side. Codebooks mapping [73-74], linear mapping [75], Neural

Networks, etc.. are used to estimate the missing components. [76-78] bring into being the

potential features of speech (voice), evaluate their performance for BWE application. In [79]

author is paying attention to various approaches for bandwidth extension namely with and

without speech (voice) production model as well as BWE with side information. The author

has listed out all the techniques of estimation of the wideband spectral envelope. In [80] the

author has designed the source-filter model based on the vocal tract to retrieve bandwidth

extended output. So in this thesis, the basic discussion that takes place here is between first

general model and baseline model. Based on that model proposed system model for B.W.E.

with flow diagram are discussed. Simulation results achieved for proposed system model in

terms of mean opinion score (M.O.S.), perceptual evaluation of speech quality (P.E.S.Q.),

spectrogram are compared with baseline algorithm, next-generation super wideband coder

algorithm.

2.4 Definition of the Problem

The problem title is " Analytical Studies Relating To Bandwidth Extension of Speech

Signal For Next Generation Wireless Communication". The problem is the B.W.E. of the

speech (voice) signal from W.B. to S.W.B. to improve naturalness, pleasantness, clarity, the

brightness of the speech (voice) signal. In next-generation wireless communication many

smart devices hold up high-quality speech (voice) communication services at S.W.B. so

B.W.E. play a important role for the system.

This can be done by:


24

Employing S.F.M. based approach by taking general model for B.W.E., baseline

algorithm as a reference to achieve B.W.E. from W.B. to S.W.B. In the approach

performance of the subjective listening test (MOS) as per ITU-T recommendation for various

speech (voice) files followed by comparisons of spectrogram of estimated S.W.B. with the

baseline algorithm & next-generation super wideband coder algorithm is carried out.

2.5 Objectives and Overview of the Research

Bandwidth extension (B.W.E.) is utilized to produce W.B. & S.W.B. as good as

voice quality at end devices without much modification in the existing environment. In other

words, one can say that the objective of bandwidth extension (B.W.E.) is to extend the

bandwidth (B.W.) of the reproduced sound by synthesizing, adding additional high-

frequency components to the received low-band width speech signal.

So the objectives of bandwidth extension(B.W.E.) are listed as follows.

To increase transparency and genuineness of recovered speech, N.B.→W.B.

Further improvement in transparency and genuineness of speech (voice) signal,

W.B.→S.W.B.

To propose an algorithm based on the source-filter model(S.F.M.) by taking a baseline

algorithm as a reference and compare it with next-generation super wideband coder in terms

of perceptual evaluation of speech quality (PESQ), mean opinion score (MOS),

complementary mean opinion score (CMOS).

Compare the spectrogram of the extended output of baseline coder, proposed coder, next-

generation S.W.B. coder and prove that results obtained based on the proposed coder are

comparable with baseline as well as a next-generation super wideband coder.

B.W.E. based on the source-filter model is a suitable choice because the illustration of

20ms (frame duration) * 8KHz (sampling frequency) -160 samples by just 12 (twelve)

parameter results in data rate reduction ultimately save the bandwidth and storage

requirements.

The digital filter and its slowly changing parameters are the key factors to achieve

compression of a speech signal.

The research work presented here can be utilized to get better speech (voice) quality

obtainable by the W.B. system and to conserve speech (voice) quality when S.W.B. devices

are used at the side of W.B. services.

Introduction

25

CHAPTER-3

Fundamental of Speech Production Model,

Types of Speech coder and its attributes

3.1 Introduction

Before studying in detail regarding B.W.E. for next-generation wireless

communication, it is essential to know the fundamental of the speech (voice) production

process as well as the data rate of a speech (voice) signal. A speech (voice) signal is defined

as the acoustic wave that is radiated from the human speech production arrangement when

air is expelled from the lungs, resulting flow of air is agitated by a constriction someplace in

the vocal tract.

Fig.3.1 represents speech (voice) production system model. A speech (voice) signal

is created by a speaker at his/her mouth or lips in the form of pressure waves. The organs

engaged in the speech (voice) creation mechanism are the lungs, larynx, vocal tract (V.T.).

The lungs generated an airflow is modulated by vocal cords or vocal folds of the larynx. The

airflow passing through the glottis-a slit-like orifice between the two vocal folds–is

translated to either a quasi-periodic or noisy airflow by the vocal folds vibration. The

resulting airflow source stimulates the V.T. that comprises oral, nasal, pharynx cavities. The

V.T. performs spectral shaping or coloring of the excitation source. The subsequent variation

of air pressure at the lips is spread out in the form of traveling waves called speech (voice)

[90]. Speech (voice) signals can be seen as the output of a filtering operation in which the

V.T. system (or filter) is excited by the modulation of an excitation source or airflow. The

mechanism is typically known as the source-filter model (S.F.M.) of speech (voice)

production which allows the modeling of speech (voice) signals as a convolution of the

impulse response of the V.T. filter, excitation source [91].

This section presents a concise overview of the different speech (voice) sounds with

their spectral characteristics, B.W. limitations imposed by telephone filter etc..



26

Figure 3.1 Speech (voice) production system model [90]

This section provides a brief overview of different speech sounds, their spectral

characteristics, bandwidth limitations imposed by telephone filter results in terms of

intelligibility,quality.

3.2 Speech (voice) sounds

Speech (voice) sounds are extensively separated into two categories: vowels, consonants

[147].

Vowels:

Vowels form the largest group of phonemes. The characteristics of vowels change based on

the position of the tongue–that mainly determine the V.T. shape – towards the front, center,

or back of the oral cavity [147].

Consonants:

Consonants form the second largest group of phonemes. It can be subcategorized into

nasals, plosives, fricatives, whispers, affricates [147].

Speech (voice) sounds

27

Nasals:

Nasals are closest to vowels, produced at the nostrils by the quasi-periodic airflow only

through the nasal cavity; the oral cavity remains constricted. Depending upon the place of

constriction that is formed by the tongue across the oral cavity, nasals are distinguished, e.g.

/m/ as in “mo” and /n/ as in “no”[147].

Fricatives are of two types.

Unvoiced fricatives:

Unvoiced fricatives (e.g., /sh/ in “should”) are described by a noise source generated

due to turbulent airflow near the V.T. constriction. The noise source is spectrally shaped

depending upon the location, degree of constriction formed by the tongue at the teeth or lips

or along the oral cavity [147].

Voiced fricatives:

Voiced fricatives (e.g. /z/ as in “zebra”) are generated by the concurrent generation of

noise at the constriction, vibration of the vocal folds. These sounds are formed by a

combination of a noisy, periodic airflow [147].

Diphthongs:

Diphthongs are produced by vibrating vocal folds analogous to vowels, however, the

V.T. does not remain steady, but varies smoothly between two vowel configurations. So

diphthongs are described by formant transitions as the V.T. articulation changes gradually

between two vowel positions. The examples of diphthongs are /Y/ as in "hide", /W/ as in

"out", /O/ as in "boy" and /JU/ as in "new[147].

Spectral characteristics of speech sounds:

Different speech (voice) sounds are distinguished by different temporal, spectral

characteristics. it can be distinguished from each other based upon their acoustic properties

like rapid transitions in spectral content, abrupt changes in amplitude, presence or lack or

combination of voicing, spectral shape attributed to the V.T. configuration [92]. Based on

spectral properties, speech (voice) sounds can be categorized into voiced, unvoiced sounds

[147].



28

Voiced sounds:

The voiced sounds are the end result of excitation of the V.T. by a quasi-periodic

glottal airflow. They are expressed by a quasi-periodic time-domain waveform with large

variations in magnitude. The periodicity is calculated in terms of the pitch period. The

magnitude spectra of voiced sounds thus exhibit harmonic structure with peaks at integer

multiples of the fundamental frequency or pitch, especially in the L.F. region. [7,91].

Unvoiced sounds:

Unvoiced sounds are differentiated by time-domain waveforms with relatively lower

magnitude than voiced sounds, however, with rapid variations as a result of the noise-like

nature of the excitation source. So, the spectrum of unvoiced speech broadens over the whole

audio spectrum [147].

3.3 Representation of Speech Signals

There are many possibilities for the discrete-time representation of speech (voice)

signals as shown in fig.3.2. These representations can be classified into two broad groups,

namely waveform representation, and parametric representations.

Waveform representations are concerned with simply preserving the “wave shape” of

analog speech (voice) signal through a sampling, quantization process. Parametric

representations are concerned with representing the speech (voice) signal as the output of a

model for the speech (voice) production. The first step in acquiring a parametric

representation is often a digital waveform representation; (speech (voice) signal is sampled,

quantized and then further processed to achieve the parameters of the model for speech

(voice) production). The parameters of the models are classified as either excitation

parameters (it is related to the source of speech (voice) sounds or vocal tract response

parameters) [146].

3.4 Classification of Coder

In past for landline communication PCM-64kbps was used. After that ADPCM-

32kbps was used to enlarge the speech (voice) channel by a factor of 2. LPC based source

coders are become popular in the world market which is good for estimating basic speech

parameters like pitch, formant, V.T. area function, etc.. It is used for representing speech

Classification of Coder

29

(voice) for low bit rate transmission or storage It requires 80000 bits for 100s speech frame

which is less than PCM-6400000 and ADPCM-3200000 bits.

The hierarchy of speech (voice) coder is shown in fig. 3.3. Speech coders differ

widely in their approaches to achieving signal compression. Based on how they accomplish

compression, speech (voice) coders are broadly classified into two categories: waveform

coders, vocoders. Also, there exists a hybrid coder made up of a combination of both the

coder to provide a good quality speech (voice) at low bit rates. The speech (voice) quality

produced by speech (voice) codec is a function of transmission bit-rate, complexity, Delay,

bandwidth. Hence when considering speech (voice) codec it is essential to consider all these

attributes [146].

3.4.1 Waveform Coding

Waveform coding techniques were the first to be investigated and are still widely

embodied in the relevant standards. In this class of coding, the objective is to minimize a

criterion that measures the dissimilarity between the original, reconstructed speech (voice)

signals, evaluated on a block-by-block basis. A block can be as small as one sample [146].

Waveform codec attempt, without using any knowledge of how the signal to be

coded was generated. Results from such codec can be improved if the predictor and

Representation

speech signals

Waveform

Representation

Parametric

Representations

Exercitation

Parameters

Vocal tract

Parameters

Figure 3.1

Figure 3.2 Representation of speech (voice) signals



30

quantizer are made adaptive so that they change to match the characteristics of the speech

(voice) being coded. This leads to Adaptive Differential PCM (ADPCM) codec.

Figure 3.3 Hierarchy of speech coders[146]

Waveform coders are most useful in applications that require the successful coding of

both speech, no speech signals. In the public switched telephone network (PSTN) successful

transmission of modem, fax signaling tones, switching signals is nearly as important as the

successful transmission of speech (voice). The most commonly used waveform coding

algorithms are uniform 16-bit PCM, commanded 8-bit PCM, ADPCM [146].

3.4.2 Source Coding

Source coders operate using a model of how the source was generated, attempt to

extract, from the signal being coded, the parameters of the model which are transmitted to

the decoder. The V.T. is represented as a time-varying filter, excited with either a white

noise source, for unvoiced speech segments, or a train of pulses separated by the pitch period

for voiced speech (voice). Therefore the information which must be sent to the decoder is the

filter specification, a voiced/unvoiced flag, the necessary variance of the excitation signal,

Classification of Coder

31

pitch period for voiced speech. This is updated every 10-20 ms to follow the non-stationary

nature of speech (voice).

The model parameters can be determined by the encoder in several different ways,

using either time or frequency domain techniques. Also, the information can be coded for

transmission in various ways. Vocoders tend to operate at around 2.4 kbit/s or below,

produce speech (voice) which although intelligible is far from natural sounding. Increasing

the bit rate much beyond 2.4 kbit/s is not worthwhile because of the inbuilt limitation in the

coder's performance due to the simplified model of speech (voice) production used. The

main use of vocoders has been in military applications where natural-sounding speech

(voice) is not as important as a very low bit rate to allow heavy protection, encryption [146].

3.4.3 Hybrid Coding

Waveform coders are capable of providing good quality speech (voice) at bit rates

down to about 16 kbit/s but are of limited use at rates below this. Vocoders on the other hand

can afford an intelligible speech (voice) at 2.4 kbit/s and below, but cannot provide a natural-

sounding speech (voice) at any bit rate.

A hybrid coder tends to act like a waveform coder for high bit-rate, parametric coder

at low bit-rate, with fair to good quality for medium bit-rate. This technique dominates the

medium bit-rate coders. The most successful, commonly used coder are time-domain

Analysis-by-Synthesis (ABS) codec. Such coders use the same linear prediction filter model

of the vocal tract as found in LPC vocoders. However, instead of applying a simple two-

state, voiced/unvoiced, model to find the necessary input to this filter, the excitation signal is

chosen by attempting to match the reconstructed speech waveform as closely as possible to

the original speech (voice) waveform [146].

3.5 Speech (voice) coding

Speech (voice) coding algorithms have the target of approximating the compact

digital representations of continuous speech (voice) signals for efficient storage, transmission

[94]. In the year 1990s, the increased number of mobile phones, demands to mobile

communications brought new challenges for digital speech transmission systems [7]. General

block diagram of speech coding is shown in fig. 3.4.. As represented in the diagram the

digital transmission of speech (voice) involves A to D converter to covert continuous data in

digital arrangement. The function of codec at the encoder side is to converts an continuous



32

signal into squeezed, binary bits pattern as indicated in fig. 3.4. This binary bits pattern can

be sent over digital wireless networks. At the recipient side, the binary bit stream is

translated back to an continuous signal using a decoder. Codec are utilized in telephones,

cellular networks, televisions, set-top boxes etc..[89].

Considering the speech (voice) codec attributes like low complexity, limited delay,

lower cost, limited radio network resources it is must be necessary to operate speech (voice)

codec at lower bit rate [93,95]. Perception plays a fundamental role in the art of lossy speech

(voice) coding. Lossy speech (voice) coding can be summarized as the endeavor to reduce

the bit rate of the coded speech (voice) through an efficient, minimal representation of the

speech (voice) signal while maintaining an acceptable level of perceived quality of the

decoded speech (voice).

Fig. 3.5 (a) and 3.5 (b) show a section of the spectrogram for an unvoiced and a

voiced sound respectively. The range from 300-3400 Hz is called the narrowband. The

complete range is referred to as wideband. The "missing" range in narrowband compared to

the wideband is referred to as the extension band. Throughout the thesis the bands will

frequently be referred to by their abbreviations; N.B., W.B.,E.B. respectively. As can be seen

in both cases, there is a considerable amount of energy in the extension band, which would

result in a significantly improved version of the speech (voice)[69].

Speech can be divided into two classes, voiced and unvoiced. The difference between

the two signals is the use of the vocal cords and V.T. (mouth and lips). When voiced sounds

are pronounced you use the vocal cords, vocal tract. Because of the vocal cords, it is possible

to find the fundamental frequency of the speech. In contrast to this, the vocal cords are not

Speech

Encoder

Transmission Speech

Decoder

Transmitter Channel Receiver

Discrete Sample s (n)

10110011…..., 64kbps

1001..,

8kbps

1001…,

8kbps

10110011…,

64kbps

D/A

Converter

Retrieved

speech s^ (t)

A/D

Converter

Analog

speech s(t)

Figure 3.4 General block diagram of speech coding

Speech (voice) coding

33

used when pronouncing unvoiced sounds. Because the vocal cords are not used, is it not

possible to find a fundamental frequency in unvoiced speech. In general, all vowels are

voiced sounds. Examples of unvoiced sounds are /sh/ /s/ and /p/.There are different ways to

detect if voices are voiced or unvoiced. the fundamental frequency can be used to detect the

voiced and unvoiced parts of speech. Another way is to calculate the energy in the signal

(signal frame). There is more energy in a voiced sound than in an unvoiced sound [69].

Figure 3.5 Voice (a) and Unvoiced (b) Speech frame representation [69]

3.6 Issues related to digital speech coding

Digital coding of speech (voice), bit rate reduction process has thus emerged as an

important area of research. This research largely addresses the following problems:

Although it is very attractive to reduce the PCM bit rate as much as possible, it

becomes increasingly difficult to maintain acceptable speech (voice) quality as the bit

rate falls.

As the bit rate falls, acceptable speech (voice) quality can only be maintained by

employing very complex algorithms, which are difficult to implement in real-time

even with new fast processors with their associated high cost, power consumption, or

by incurring excessive delay, which may create echo control problems in system.



34

To achieve low bit rates, parameters of speech production and/or perception model

are encoded and transmitted [96].

Speech quality as produced by speech codec is a function of transmission bit rate,

complexity, delay, B.W.. Therefore when considering speech (voice) codec it is essential to

consider all these attributes. For a given application some attributes are predetermined while

trade-off can be made among others. For example, low bit rate speech codec tends to have

more delay as compared to the higher-bit-rate codec. They are generally more complex to

implement, often have lower speech quality than the higher-bit-rate codec [146].

3.7 Speech codec attributes

Speech codes are characterized by five general attributes:-

3.7.1 Transmission bit rate

Specification of how many bits are required to describe one second of speech. The bit

rate is a measure of how much the speech model has been exploited in the coder; the lower

the bit rate, the greater the reliability on the speech production model. Depending on the

system and design constraints, fixed-rate or variable-rate speech coders can be used [146].

3.7.2 Speech Quality

The speech quality has the most important dimensions among all the attributes. The

decoded speech should have a quality acceptable for the target application. Bit rate, quality

are intimately related: the lower the bit rate, the lower the quality. While the bit rate is

inherently a number, it is difficult to quantify. The most widely used subjective measure of

quality is the Mean Opinion Score (MOS), which is the result of averaging opinion scores

for a set of untrained subjects (listeners). Each listener characterizes each set of utterances

with a score on a scale from 1 to 5 (1-bad, 2-poor, 3-fair, 4-good, 5- excellent). The MOS

quality rating is shown in Table 3.1. A MOS of 4.0 or higher defines good or toll-quality,

where the reconstructed speech (voice) signal is generally indistinguishable from the original

signal. A MOS between 3.5 and 4.0 defines communication quality, which is sufficient for

telephone communications. The most widely used objective measure of quality is the signal-

to-noise- ratio (SNR), SNR indicates how well the waveforms match but do not account for

how the decoded speech (voice) is perceived [146].

Issues related to digital speech coding

35

Table 3.1 MOS Quality Rating

Quality scale Score Listening effort scale Impairment

Excellent 5 No effort required Imperceptible

Good 4 No appreciable effort

required

Perceptible but not

annoying

Fair 3 Moderate effort required Slightly annoying

poor 2 Considerable effort

required

Annoying

bad 1 No meaning understood

with reasonable effort

Very annoying

3.7.3 Bandwidth

The B.W. of the speech signal that needs to be encoded is also an issue. Typical

telephony requires 200–3400 Hz B.W. Wideband speech coding techniques (useful for audio

transmission, teleconferencing, tale teaching) necessitate 7–20 kHz bandwidth [146].

3.7.4. Communication Delay

The measure of the time taken for an input sample to be processed at the encoder,

transmitted, and decoded at the decoder. Delays over 150 ms can be unacceptable for highly

interactive conversations. Coder delay is the sum of different types of delay. The first is the

algorithmic delay arising because speech coders usually operate on a block of samples

(frames), which needs to be accumulated before processing can begin. The computational

delay is the time that the speech (voice) coder acquires to course of action the frame. In

practice, the total delay of many speech coders is at least three frames[146].

3.7.5 Complexity

Complexity is defined as The measure of computation (memory) required to

implement the coder. The computational complexity, memory requirements of a speech

(voice) coder determine the cost, power consumption of the hardware on which it is

implemented. In most cases, the real-time operation is required at least for the decoder. A

large complexity can result in high power consumption in the hardware [146].



36

3.8 Speech codec for next-generation wireless communications

Selection of Particular speech (voice) codec plays a vital role in design of digital

next-generation wireless communications. it a challenging task to shrink speech (voice) to

maximize the number of user on the system. The other important parameters are encoding

delay, complexity in design of encoder, necessity of power requirement, compatibility with

on hand standards, robustness of the coded speech (voice) to transmission errors etc...[146].

3.9 Speech (voice) quality, intelligibility

In a telephone chat, the acoustic signal is recognized by the near-end user's ear, origin

an auditory event in the brain results in a depiction of the voice (sound) [96]. former

knowledge of the communication system, emotions of the listener influence the quality

judgment [13,97]. According to [98] speech (voice) quality encompasses attributes such as

naturalness, clarity, pleasantness, brightness. Intelligibility is considered as a other

dimension to measure speech (voice) quality [7]. Besides the traditional N.B. (300–3.4kHz)

speech coding, transmission, W.B. (50–7kHz), S.W.B. (50–14kHz) speech transmission has

been deployed in many telephone applications..

3.9.1 Listening-only tests

Listening-only tests can play a vital role in assessing speech (voice) quality of a

communication system. This test can be done by means of pre-recorded, processed set of

speech (voice) samples (through headphones). The subjects are requested to assess the

quality using a predefined scale given by the experimenter. An advantage of listening-only

tests is that the evaluation is subjective, i.e. subjects listen to real speech samples, grade them

according to their personal opinion. A drawback listening-only tests is that test design

factors, rating procedures affect the subject's observation procedure. As shown in table 3.2.,

subjects are asked to assess the speech (voice) quality using the 5-point mean opinion scale

(MOS) for absolute category rating (ACR) test [99]. In degradation category rating (DCR)

test the subject is asked to assess a degraded signal w.r.t. reference signal using the

degradation mean opinion score (DMOS) as shown in table 3.3.

Speech codec for next-generation wireless communications

37

Table 3.2 MOS as per recommendation ITU-T P.800 [99]

Score Quality of speech

5 Excellent

4 Good

3 Fair

2 Poor

1 Bad

Table 3.3 Degradation means opinion score as per recommendation ITU-T P.800 [99]

Score Degradation is

5 Inaudible

4 Audible but not annoying

3 Slightly annoying

2 Annoying

1 Very annoying

In the comparison category rating (CCR) test, two signals are judge against each

other as shown in table 3.4

Table 3.4 Comparison means opinion score according to ITU-T P.800 [99]

Score Quality of the second compared

to that of the first

3 Much better

2 Better

1 Slightly better

0 About the same

-1 Slightly worse

-2 Worse

-3 Much worse

3.9.2 Conversational tests

In comparisons with previous tests, conversational tests are much close to real

conversation situation. The test can be utilized to assess either a specific source of

degradation, such as delay or echo, or the overall quality of a transmission system. In test,



38

two subjects at a time are situated in separate soundproof rooms. The subjects can be experts,

experienced, or naive participants depending on the rationale of the test. During the test, the

subjects have a dialogue on a given topic or task, give their opinion on the speech (voice)

quality [100].

3.9.3 Field tests

This test is utilized to assess speech (voice) quality of transmission systems in most

realistic environment. For example, the speech (voice) quality of a wireless phone might be

judged during true phone calls. The arrangement of such a test is expensive during system

process development. so quality tests are typically arranged in laboratory.

3.9.4 Intelligibility tests

It is one types of subjective test computed as correctly recognized speech (voice)

sounds at the receiver side of a system in percentage. In rhyme tests rhyming words are

utilized[106]. The diagnostic rhyme test (DRT) consists of 96 rhyming word pairs that differ

in their initial consonant [107]. The modified rhyme test (MRT) contains 50-word lists of six

one-syllable words, differing in either the opening or the closing consonant [108]. To find a

presentation level for test speech (voice) speech reception threshold (SRT) test can be

utilized. In this test it is necessary for a listener to understand the speech (voice) correctly a

specified percentage of the time, usually 50%.

3.10 Objective quality evaluation

Objective quality estimation techniques are computational tools build-up to assess the

quality of speech (voice) signals. Objective quality assessment methods are often classified

into two parts namely parameter based, signal-based models [101-102]. Parameter-based

models, such as E model [103], rate the important quality of a whole transmission path. They

provide information on the whole network, helpful in planning of network. Signal-based

methods play a very important role in speech (voice) enhancement field. The perceptual

evaluation of speech (voice) quality (PESQ), normalized in ITU-T Recommendation P.862

[104], can be employed to assess N.B./W.B/S.W.B. quality. Both the degraded and a

reference signal are required for evaluation of speech (voice) quality. An extension of the

PESQ is presented in the recommendation P.862.2 [105].

Speech (voice) quality, intelligibility

39

3.11 Effect of B.W. on speech (voice) quality, intelligibility

Selection of cut-off frequencies, telephone filter characteristics specified in ITU-T

recommendation G.120 [109] was relies on the B.W. limitations obliged by continuous

transmission systems. The selections were motivated by outcomes attained from subjective

listening tests [11]. To retain compatibility with existing analog PSTNs, initial progress in

digital telephony has occurred with B.W. constraints of 0.3-3.4kHz. After digitization of

speech (voice) transmission, speech (voice) coding techniques were developed to condense

speech (voice) signals to lower bit rates than PCM, without degrading speech (voice) quality.

So the bandwidth available for speech (voice) transmission was dictated by development of

cellular systems, speech (voice) coding techniques. However, the frequency content of

speech (voice) signals ranges from 60Hz to 20kHz.

The drawbacks upon the telephone B.W. thus result in a lack of distinctive spectral

properties of speech (voice) sounds. Some fricatives such as /f/ and /s/ differ in the location

of the lowest spectral peak, which occurs typically around 2.5kHz, 4kHz for a male adult

speaker correspondingly. Such distinctive properties are vanished in typical telephone speech

(voice), which often causes difficulties to the listener in distinguishing between different

fricative sounds [15]. Some plosives (/t/ and /d/) are characterized by higher energy bursts

occurring around 3.9kHz. Others (/b/, /p/) also exhibit similar energy bursts albeit with less

intensity. These distinct properties are vanished in N.B. speech (voice) results in trim down

intelligibility, naturalness for plosives. Nasals are too affected. They are dominated by the

first formant F1 which typically occurs around 250Hz. This is also vanished due to the lower

cut-off of the telephone band.

During a telephone call, occasionally H.F. sounds such as /f/ and /s/ (or /p/ and /t/)

are hard to distinguish. Improvements in intelligibility are thus essential to shrink the

listening effort, to afford comfortable communications [6]. The perceived quality as well as

the intelligibility of speech (voice) signals enhancing with increases in acoustic B.W.,

particularly for unvoiced sounds. This is because they restrain a substantial portion of their

spectral content beyond 3.4kHz. A changeover from N.B. to W.B. communication then

obviously leads to an raise in syllable, sentence intelligibility from 90% ,99.3% to 98%,

99.9% correspondingly [110-111]. The quality of communication is further improved at

S.W.B.

Development of Bandwidth Extension Model For W.B. To S.W.B.

40

CHAPTER-4

Development of Bandwidth Extension Model

For W.B. To S.W.B.

4.1 Introduction

Before studying in detail regarding development of bandwidth extension model for

W.B. To S.W.B. general model for B.W.E. from N.B. to W.B., bandwidth limitation in

compressed speech/audio, High-Frequency Bandwidth Extension (H.F.B.E.) algorithm [120]

are discussed. The B.W.E. approaches can be categorized into two parts namely Non-model

based B.W.E., source-filter model based B.W.E..

4.2 Non-model based B.W.E. approaches

The non-model based B.W.E. approaches do not use a priori knowledge about speech

(voice) production mechanism and employ simple operations to introduce missing frequency

components. The operations include spectral translation or shifting (fixed or adaptive),

generation of high-frequency components via non-linear operations on time-domain signals,

band pass filtering on white noise, etc. Some approaches also perform spectral shaping of the

generated frequency components using some gain control parameter or an empirically

determined filter. The first commercial use of such B.W.E. methods by the British

Broadcasting Corporation (BBC) [112] is reported in the year 1972 where the acoustical

bandwidth of telephone speech in broadcast programs is improved. The notable examples are

[113-116]. Such methods, however, produce extended W.B. speech (voice) signals with

audible processing artifacts and distortions. This is because the energy of the generated

frequency components is either too weak or too strong compared to the N.B. component

[117]. Additionally, the quality of extended speech (voice) signals depends upon the

effective bandwidth of the original input signal [118].

Introduction

41

4.3 B.W.E. approaches based on the source-filter model

The model-based B.W.E. algorithms use a priori knowledge about characteristics of

speech (voice) signals, human speech (voice) production mechanism. Since the beginning of

the 90s, most B.W.E. algorithms started exploiting the classical source-filter model (S.F.M.)

[119] of speech (voice) production where an N.B./W.B. speech signal is represented by an

excitation source, vocal tract filter. The frequency content of these two components can be

extended through independent processing before a W.B./S.W.B. signal is resynthesized.

The extension of N.B./W.B. speech is thus divided into two tasks;

Estimation of H.B. or W.B./S.W.B. spectral envelope from input N.B./W.B. features

via some form of an estimation technique

Generation of H.B. or W.B./S.W.B. excitation components via some form of time-

domain non-linear processing, spectral translation or spectral shifting methods. The

H.B. component is usually parameterized with some form of linear prediction (LP)

coefficients whereas the N.B./W.B.component is parameterized by a variety of static

and/or dynamic features

4.3.1 B.W.E. from N.B. to W.B. Speech Conversion:

In Digital Signal Processing signals are band-limited w.r.t. use of sampling

frequency. N.B., W.B., S.W.B. speech has a sampling frequency of 8kHz,16 kHz,24 kHz

respectively. Based on N.B. to W.B. speech conversion and baseline algorithms (H.F.B.E.)

proposed method for W.B. to S.W.B. has been discussed. It is a blind method because they

estimate missing H.F. component from available L.F. components. The proposed flow chart

of bandwidth extension based on baseline and proposed algorithms discuss the achievement

of W.B./S.W.B. speech signal at the receiver side without actually transmitting W.B./S.W.B.

4.3.2 General Model for B.W.E.:

The fundamental thought adopted here for the B.W.E. system is the separate

extension of the spectral envelope and the residual signal as depicted in fig.4.1. First, the

incoming telephone-band signal is analyzed through the LPC-analysis. Based on the spectral

envelope, telephone-band short time features, the spectral envelope is extended. The

extended version is essential to define the shaping filter characteristic. Second the extended


42

residual signal for this shaping filter is calculated from the telephone-band residual signal as

highlighted in fig.4.1. According to the linear speech production model the synthetic signal

is generated by driving the shaping filter with the extended residual signal. The resulting

power of the synthetic signal has to be matched to the telephone-band signal power such that

both signals can be added to build the desired W.B. signal [70].

Figure 4.1 General Model for B.W.E.[70]

4.3.3 Bandwidth Limitation in Compressed Speech/Audio:

Most perceptual speech/audio codec B.W. limits the input signals to use the available

bits for psycho acoustically relevant L.F. signals. The B.W. limitation becomes severe at

very low bit rates e.g. 128kbps mp3 encoded speech/audio is bandwidth limited to about 15

B.W.E. approaches based on the source-filter model

43

kHz and 64kbps mp3 to about 8 kHz. Fig. 4.2 shows an example of a 96 kbps mp3 encoded-

decoded signal spectrum, the signal spectrum chopped beyond 11 kHz. These missing H.F.

signals cause speech/audio signals to sound dull and at times muffled. so it is becoming

essential to improve audio quality in such cases by artificially creating high frequencies from

low-frequency information[120].

Figure 4.2 Bandwidth Limitation in Compressed Speech/Audio [120]

Issues with the Approaches:

Bandwidth limitation becomes severe at very low bit rates.

Proposed Solution

Encoder side modifications solve the bandwidth limitation issues.

Internal Issue

An enormous amount of content available which suffers from the above-mentioned

quality issues.

Proposed Solution

Post-processing methods after decoding Ex..spectral extrapolation

Internal Issue

Computationally too expensive

Final Solution-

Employ Time-Domain Method for real-time Implementation.


44

4.3.4 Baseline System Model for B.W.E.

As shown in fig.4.3 baseline system model accepts decoded data as input from any

lossy audio decoder and recreates high frequencies blindly i.e. by using only the decoded

audio signal and nothing else. Since the bandwidth of the input signal is unknown it is first

estimated by using a real-time bandwidth detection process. After detecting the highest

frequency present in the signal at any given time, the decoded signal is divided into sub-

bands up to half the detected highest frequency of the signal. Each sub-band signal is then

individually passed through non-linearity to generate harmonics. The generated harmonics

are gain scaled to achieve spectral envelope shaping and added back to the original

signal[120].

Figure 4.3 High-Frequency Bandwidth Extension (H.F.B.E.) Algorithm [120]

Component Description of the Baseline System Model for B.W.E.:

Bandwidth Detection:

Bandwidth detection involves analyzing the signal in real-time e.g. every 20msec to

find the highest frequency present in the signal. A typical range of bandwidth of interest is

from 7 kHz to 16 kHz. Signals with B.W. below 7 kHz might means that the original audio

signal does not contain high frequencies[120].

B.W. beyond 16 kHz means that there are a significant amount of high frequencies

present and reconstruction is not required. A possible solution for bandwidth detection might

be to perform an FFT on the decoded audio data. Using the frequency response it would be

simple to detect the highest frequency with any significant energy. But an FFT would

increase the complexity of the reconstruction algorithm and application to various platforms

Sub-band

Filtering

Non-Linear

Processing Post-

Processing

Bandwidth

Detection

Bandwidth Limited

Input(WB) Bandwidth Etended

S.W.B. Output

Delay


45

would be difficult. Instead of using an FFT, we could have a bank of band pass IIR filters

followed by the energy calculation of filtered results. For 1 kHz accuracy in detection, we

need to have 8 band pass filters in our frequency range of interest[120].

Sub band filtering:

The B.W. detection step is followed by sub-band filtering in which the input signal is

divided into multiple bands before the nonlinear processing step. It is well known that any

nonlinear operation produces harmonics of the input frequency along with intermediation

noise. The more the frequency contents are present in the signal before the nonlinear process

the more the intermediation noise. Hence if we band pass filters the input signal into multiple

bands and applies nonlinear processing individually on each sub-band the intermediation

noise would be reduced. The higher the number of sub-bands the lesser will be the

intermediation noise. In our implementation, we divided the signal into 2 sub-bands.

Although a higher number of sub-bands would have been desirable, the complexity of

implementation would have been significantly increased. If B is the B.W. detected then the

1st band extends from 0.5xB – 0.75xB and 2

nd band from 0.75xB – B. The 1

st and 2

nd sub-

bands are called Band1 and Band2. Since the detected bandwidth is approximated to be

within a set of fixed values, the filter coefficients used for sub-band filtering are

precalculated and stored. 4th

order IIR filters were found to be sufficient for the

purpose[120].

Non linear processing:

The sub-band filtered data is then passed through a non-linear process to detect

harmonics. Any non-linear process which generates second harmonic can be used. Full-wave

rectification is a suitable non-linear process. Along with the very low complexity of

implementation, it also exhibits harmonic signal amplitude linearity [121]. The full-wave

rectification process is followed by band pass filtering to filter out frequencies outside the

range of interest generated due to intermediation and aliasing. Band1 harmonics are post

filtered from B – 1.5xB and 2nd sub-band harmonics from 1.5xB – 2.0xB. The result of the

post-filtering of Band1 data is called Band3 and that of Band2 is called Band4. Fig. 4.4

shows the bands Band1, Band2, Band3, and Band4. If B is detected greater than 9 kHz, the

maximum recreated frequency is limited to 18 kHz.


46

Post processing:

The goal of the post-processing step is to modify the energy of the recreated bands

Figure 4.4 Input and Reconstruction band[120]

to have them match the original frequency content. A simple algorithm that attempts to

maintain the continuity of the spectral envelope by modifying energies of Band3 and Band4

has been developed. After the nonlinear processing and post-filtering step the energy in

bands Band1, Band2, Band3, and Band4 are evaluated on a short time basis. The energy of

the original signal's sub-band Band1 is called E1, that of Band2 is called E2 and those of

reconstructed Bands 3 and Band 4 are E3 and E4 respectively. Assume that E3Target and

E4Target are the desired energies of Band3 and Band4 respectively which would give a smooth

reconstructed spectral envelope. From our experiments, we found that the desired value of

E3Target is approximated by

1

22

3TargetE E

EE

.................................. (1)

3

3Target G 3

E

E

......................... (2)

2

3Target3Target

4TargetE E

EE

................ (3)


47

4

4Target

4

G

E

E

............................... (4)

The reconstructed sub-bands Band3 and Band4 are then gain modified using G3 and

G4 from equations (3) and (4) and added back to the original signal to get the B.W.E. output

signal [120].

4.4 Detailed analysis of B.W.E. based on proposed S.F.M.

Fig. 4.5 depicts the proposed S.F.M. based algorithm block diagram for W.B. to

S.W.B. speech conversion. The block diagram is mainly categorized into four parts as

narrated below:

The first and main important step is to acquire the W.B. input signal from the pre-

processing stage and the framing & windowing is performed on the W.B. input signal.

In the second steps the output of the first step is carried out by the LP algorithm to

divide the signal into two parts namely spectral information and residual error signal, so one

can say that missing H.F. components are estimated from accessible L.F. components.

In the third step original L.F. component is extracted from the input W.B. frame by

zero insertion.

In the final step both the L.F. and H.F. components are added together to get

estimated S.W.B. output.

Detailed Analysis of Proposed System Model:

Framing:

Partitioning of a speech signal into frames is the first basic component of our

proposed Input from Preprocessing approach. on the whole, a speech (voice) signal is not

stationary, but it is typically is stationary in windows of 20 milliseconds. Therefore the

signal is divided into frames of 20 milliseconds which corresponds to n1 samples [122].

sst ftn *1 ..........................(5)


48

Xamr= xwb

Figure 4.5 Block diagram of the proposed approach for W.B. to S.W.B.

speech (voice) signal

Input from

Preprocessing

p

k

kwbzak1

1

1

Framing

LP

Analysis

Analysis

Windowing

Zero

insertion

FFT jwezZH |)(

HPF

Zero

Insertion

Synchronization

IFFT

Synchronization

Estimated

SWB XSWB

LPF

+

×

Detailed analysis of B.W.E. based on proposed S.F.M.

49

Fig.4.6 depicts a pictorial representation for framing the signal into four frames of 20

ms[122]. Fig.4.7 illustrates a flow chart demonstration for framing where the signal is

divided into frames, finds out the number of samples in a frame, and plot the frames through

the audio read function in MATLAB is carried out.

Figure 4.6 Pictorial representation for framing [122]

Windowing:

During frame blocking, there is a possibility that signal discontinuity may arise at the

beginning and end of each frame. To reduce the signal discontinuity at either end of each

block the next step employed is windowing. The window function exists only inside a

window and evaluates to zero outside some chosen interval. When multiplied with the

original speech frame, the window function taper down the beginning and the end to zero

and thereby minimize spectral distortion at both the end. The simplest rectangular window

Figure 4.7 Flow chart representation for Framing

Using audio read function read the given wave file

Select 25 ms duration of frame and

sampling frequency= 16 kHz

Plot speech(voice) signal

Find out the number of sample in one frame and total

number of frames in wave file

Plot each frame by processing data of specified

duration of frames

uration of frames


50

function is given by

otherwise

1Nn0

0,

1,ω(n)

......................(6)

A more commonly used smoother function (Hamming window) is defined as

otherwise

1Nn0,

0,

1N-

πn2cos0.46-0.54

ω(n)

...............(7)

where n= sample no. in a frame, N= total no. of samples & 10-30ms window length[122].

Linear Prediction:

Linear prediction (LP) is the heart of the bandwidth extension algorithm for N.B. to

W.B. and W.B. to Super wideband (S.W.B.). In linear prediction, the present sample can be

estimated as a linear combination of past samples. Fig.4.8 and Fig.4.9 represent all-pole

spectral shaping synthesis filter and linear prediction (LP) model of speech(voice) creation

respectively. Naturally, three filters namely glottal pulse model G(z), vocal tract(VT) model

V(z), and radiation model R(z) are utilized to model the speech creation. The glottal pulse

model(GPM) contour the pulse train before it is used as input to V(z). Three models together

can be represented via single T.F. H(z), i.e..

H(z) = G(z)V(z)R(z) ..................(8)

Where H(z) is called as the synthesis filter and is shown in "Fig.5", Obviously the

synthesis filter can be represented via the inverse of the analysis filter, i.e.:

H(z) = 1/A(z) .................. (9)

In this way, we can parameterize a voice signal and it is a suitable and precise method [97].

Figure 4.8 All pole spectral shaping synthesis filter [97]

Excitation E(z) Synthetic speech

S(z) = E(z) * A(z)

)(

1 H(z)

zA


51

Figure 4.9 Linear prediction (LP) model of speech(voice) creation[97]

Fig.4.10 represents the LPC based standalone B.W.E. algorithm (without sideband

information). It is a blind approach in the sense that there is no requirement of additional side

information to be sent. so no additional data rate burden occurs in this approach.

Bandwidth extension here can be divided into two tasks [123]:

Estimation of the W.B. spectral envelope

Extension of the excitation signal.

evaluation of the W.B. spectral envelope can be attained by a variety of techniques like

Codebook, adaptive Codebook mapping, Gaussian Mixture Models (GMM), etc...

x

Sn ARᶺw

unᶺ uwᶺ

Figure 4.10 LPC based B.W.E [123]

Feature

Extraction

Exertion

of excitation

Synthesis

1/Aᶺ (z)

Prior

Knowledge

Estimate wideband

envelope

Interpolator Analysis

Aᶺ (z)

N.B. Signal Estimated

W.B.signal


52

Codebook Mapping:

The codebook mapping techniques make use of two codebooks constructed in the

training phase utilizing vector quantization to find a representative set of N.B. and W.B.

speech frames. A W.B. codebook contains several wideband spectral envelopes that are

parameterized and stored as, e.g., LPC coefficients or line spectral frequencies (LSFs). An

N.B. codebook contains the parameters of the corresponding N.B. speech frames. When the

bandwidth extension system is used, the best matching entry for each N.B. input frame is

identified in the N.B. codebook and the spectral envelope of the extension band is generated

using the corresponding entry in the W.B. codebook. This technique was proposed in [124],

and presented in [125] with refinements. Separate codebooks were put up for unvoiced,

voiced fricatives as mentioned in [125], which boost the quality of the spectral envelope.

Also, a codebook mapping with interpolation was accounted in [126] to boost the extension

quality, Furthermore, codebook mapping with memory was implemented by interpolating

the current envelope estimate with the envelope estimate of the previous frame[127].

Linear mapping:

Linear mapping is utilized in [128] to estimate the highland spectral envelope. The

linear mapping between the input and output parameters (LPCs or LSFs) is denoted as

WX= Y ..............(10)

Where the matrix W is acquired via an off-line training procedure with the least-squares

method that diminishes the model error (Y–WX) by means of a training data with N.B.

parameters is indicated by a vector x = [x1, x2, . . . ,xn] ,corresponding wideband envelope to

be estimated by another vector Y = [y1, y2 . . . ,ym]. To better imitate the non-linear

correlation between the N.B. and H.B. envelopes, modifications for the basic linear mapping

method have been presented. The mapping can be realized by several matrices instead of

using a single mapping matrix. In [129], input data are clustered into four clusters, and for

every cluster, a separate mapping matrix is created. In [128], Objective analysis was showing

that spectral distortion (SD) for codebook mapping, neural network was bigger compare to

piecewise linear mapping. The objective comparison specified in [126] indicates that the

performance of codebook mapping is better than linear mapping.


53

Gaussian Mixture Model:

In linear mapping, only linear dependencies between the N.B. spectral envelope, H.B.

envelope are exploited. Non-linear dependencies can be embraced in the statistical model by

employing the Gaussian mixture model (G.M.M). A G.M.M. approximates a probability

density function as a sum of several multivariate Gaussian distributions, a mixture of

Gaussians. G.M.M. are used in B.W.E. to model the joint probability distribution of the

parametric representations of the N.B. input, extension band. Given the input feature vector

of a speech (voice) frame, the parameters representing the extension band envelope are

determined using an error estimation rule.

The G.M.M. is utilized directly in envelope extension to estimate W.B. L.P..C

coefficients or L.S.F.s from corresponding N.B. parameters [130]. The act of the GMM

based spectral envelope extension was then augmented by using Mel frequency cepstral

coefficients (M.F.C.C.) instead of L.P.C. coefficients [131]. The G.M.M. mapping with

memory further results in better performance in terms of perceptual evaluation of speech

quality (P.E.S.Q.) [132].The benefit of the G.M.M. in envelope extension methods is that

they present a continuous estimation from N.B. to W.B. features in comparisons with

discrete acoustic space resulting from vector quantization (V.Q.). Better results were

reported for G.M.M. based schemes compared to codebook mapping in [130] in terms of

S.D, cepstral distance, a paired subjective comparison.

Extension of the excitation signal.

Extension of the excitation process is an imperative feature of any W.B./S.W.B.

enhancement scheme is the generation of the H.B./W.B. speech excitation. The residual

extension process mainly claims to double the sampling rate, from 8 to 16 kHz, as well as to

uphold the whole spectrum flat. The harmonics contained in the N.B. residual should also be

carried forwarded in the W.B. residual. Forthcoming are few of the popular techniques which

are referred to in this research for bringing forth the H.B./W.B. excitation signal from the

given input N.B. excitations (N.B. residual signal).

Spectral folding (SF)

Spectral translation (ST)

Nonlinear distortion (NLD)


54

Spectral folding (SF):

Spectral folding is a time-domain approach for extension of excitation and is one of

the most conventional, popular methods because of its simplicity, wide usage in high-

frequency excitation regeneration in B.W.E.. Inserting zero between each sample of N.B.

residual signal, folds the baseband spectrum to higher frequencies. Thus, the up-sampling

extends the signal bandwidth from 4 to 8 kHz. Spectrum folding results in the mirrored

image of the N.B. spectrum at the spectrum of H.B. but the major disadvantage of this

method is that the harmonic structure leaves a spectral gap at 4 kHz [133,134].

Spectral translation:

Spectral translation creates a shifted spectral version of the N.B. residual at the high-

frequency band. This approach prompts input N.B. residual to mix with (-1)n, (where n is the

index of each sample) which is rather similar to modulation of any signal with a frequency

equal to Nyquist frequency. This modulated signal is up-sampled, high pass filtered, added

to the up-sampled, low pass filtered N.B. residual to yield W.B. residual at the output. The

major shortfall of this approach is that it fails to preserve the original N.B. residual

information [134].

Nonlinear distortion (NLD):

In the NLD approach, initially, the N.B. residual is up-sampled by factor two and

then delivered to a nonlinear function as expressed in Eq.11. The desired bandwidth and

harmonic structure can be obtained over the whole spectrum from the resulted distorted

signal. After the whitening filter, the resulting signal spectrum is then flattened so that the

excitation does not affect the overall spectral shape. Finally, the output meets the

requirement of the W.B. residual. A simple nonlinear function can be illustrated by:

2/)]()1()()1[( (t) txtxy .................(11)

where x(t)= input signal, y(t)= distorted output signal, and α is a parameter between 0 and 1.

When α = 1, it becomes the absolute value function. Furthermore, after varying the values

within a specific stipulated range on the trial and error basis in this work, it is witnessed that

the value of α = 0.7 offers overall promising and better results[134].


55

H.F. & L.F. component addition to getting Estimated SW.B. speech signal:

As discussed in Step 2 of the proposed system model for B.W.E. after framing,

windowing, L.P. analysis the signal is divided into two different parts namely spectral

envelope estimation and residual error signal. Both parts are processed separately as shown

in the fig.4.5 and shaped by shaping filter and High pass filter to get the H.F. component.

The Band limited signal at the input side is resampled via zero insertion to get the L.F.

component. After getting both components they are added together via the over-lap add

method to get the estimated SW.B. output.

Fig.4.11 Depicts the proposed flow for W.B. to S.W.B. indicating subjective,

objective measurement. As shown in the flow diagram first the simulation parameter is

defined and the required filter is loaded. After that, the speech files AMRWB, EVS, S.W.B.

are read by utilizing audio read function and it is passed through the H.F.B.E. algorithm

function as shown in Fig.4.12. H.F.B.E. baseline algorithm first W.B. signal is up sampled

via zero insertion, low pass filter. After that to extract the highest octave from the up-

sampled W.B. signal, find out the absolute value of function bandwidth detection, sub-band

filtering, N.L.D. devices are utilized. After that to capture the required parts of the spectrum,

omit the remaining part post-processing with Filtering is utilized. Finally, it is added with a

delayed version of input to get the required S.W.B. output. Fig.4.13 depicts the proposed

algorithm proposed flow chart for obtaining the estimated S.W.B. signal. The basic

difference between the baseline and the proposed approach is that in baseline only AMRWB

input, filter, and gain parameter are processed while in the proposed approach three

parameters like LPC order, NFFT Points, window length of S.W.B. are additional parameter

to be processed. In the proposed method input W.B. Signal framing, separation of a framed

signal into a spectral envelope & residual error, finding of missing H.B. components is done

in a loop which is to process for the whole number of frames. Then after adjustment of delay

H.B. and N.B. components are added to get the estimated S.W.B. signal. Spectrogram,

objective & subjective measurement are performed on the estimated S.W.B. signal w.r.t.

input wave files.

All experiments reported in this thesis were carried out by utilizing voice records

from The CMU file (database) [135] & TSP file (database) [136] at different sampling rate

fs. Fig.4.14 demonstrates the data pre-processing, assessment for W.B. to S.W.B. TSP, CMU

ARCTIC were down sampled to S.W.B. signals databases so that both the databases have a

common fs of 32kHz. Down sampling can be done employing the Resamp-Audio tool


56

Figure 4.11 Proposed Flow for W.B. to S.W.B. indicating Subjective and

Objective measurement

Figure 4.12 Baseline algorithm proposed flow chart

Band limit extended speech file to 15kHz

Read speech files

Remove few samples at the start and end to remove

inconsistencies for initial and last 2 frames

Subjective & Objective measures foe given speech files

Plot and analyze various spectrograms

Load filters

Define Filter delays

Extract highest octave from the up sampled WB

signal & find out absolute value using function

abs

Up sample input WB signal

Synthesize extended speech

Extract HB speech

Define Input and output parameters


57

for m = 0 :Nframes-1

Loop Ends

Figure 4.13 Proposed Algorithm Proposed Flow Chart

contained in the AFsp package [137]. The voice level of all utterances in both files

(databases) maintained to 26dBov [138] to produce Xswb. After that next-generation voice

coder (EVS) encoding [139] is applied to produce Xevs, Xswb. down sampled to 16kHz and

Define Filter delays

Input WB signal framing

Carry out the overlap-add FFT processing:

Excitation extension via zero insertion

Get WB LP spectral envelope and residual/excitation

Define Input and output parameters

Get NB components from up sampled WB signal

Resynthesize the HB speech

Combine HB and NB components

Adjust delays


58

processed through BPF [140] as per recommendation P.341, so finally we can get the data

Xwb on which AMRWB coding [141] has been applied to produce Xamr, (Xwb in fig.4.5 is

replaced by Xamr). As shown in fig. 4.5 Xamr is the input to baseline/Proposed Algorithm to

get the Estimated S.W.B. output after processing the signal through the Algorithm. The

proposed B.W.E. algorithm is to evaluate and compare to AMRWB and next-generation

voice coder (EVS) processed voice signals, baseline H.F.B.E. algorithm [120].

XSWB Xevs

Xamr

XᶺSWB

Figure 4.14 Data Pre-Processing, Assessment for W.B. to S.W.B.

TSP

Database

P341

CMU

ARCTIC

Database

EVS

AMR-

WB

Down

sample to

32 kHz

Down

sample to

16 kHz

Level

Alignment

Baseline

/Proposed

Algorithm

Introduction

59

CHAPTER 5

Linear prediction analysis and synthesis

5.1 Introduction

This chapter starts with a basic block diagram representation of speech file

compression based on LPC analysis & Synthesis followed by a source-filter model for sound

production. N.B. to W.B. speech conversion based on LPC, Linear prediction autoregressive

modeling comparisons, Mathematical analysis of LPC model, stability consideration of LP.

5.2 Speech file Compression based on LPC

Fig. 5.1 represents the complete block diagram showing LPC co-efficient obtained

from input speech(voice) signal. First, the input signal is divided into a frame duration of

20ms duration followed by windowing to remove discontinuity and taper down the edge.

After windowing speech (voice) signal is given to linear predictor to find out LPC co-

efficient. Thus, instead of transmitting the PCM samples, parameters of the model are sent to

achieve compression.

Figure 5.1 LPC co-efficient obtained from Input Speech Signal [some

part adapted from [142]


60

Fig.5.2 depict the detailed analysis of speech file compression based on LPC analysis &

synthesis. As shown in fig. 5.2 compression of the speech file from the original speech file based

on LPC can be done in seven steps as below:-

(1) Speech file-This is a speech file in .wav form. This may be any arbitrary speech file

given as an input to the next module.

(2) Divide into the frames-This module will divide each .wav file into 20-30 ms frames.

Each frame generated in this way will be given as input to the next module for its analysis.

The frames obtained in this way are given as input to the function.

(3) Find parameters- This module will collect some of the data required for regenerating

the original input signal (voiced/unvoiced, gain, pitch). High amplitudes of the voiced

sounds and high frequency of the unvoiced sounds will be used to determine whether a

sound is voiced or unvoiced. An algorithm called the Average Magnitude Difference

Function (AMDF) will be used for pitch period estimation. The gain of the filter will also be

determined.

(4) LPC technique-This module will generate the remaining data required to reconstruct the

original speech signal. This data is the coefficients of the synthesis filter. These are to be

calculated using the Autocorrelation method. In this method using the concept of minimum

prediction error, a matrix is obtained where the variables are the filter coefficients. The

solution of the matrix obtained in the Autocorrelation method will be found using the

efficient Levinson Durbin algorithm. This algorithm can be used because of the special

properties of the Autocorrelation matrix. It reduces the complexity of multiplication

.

(5) Regenerate frames-This module will consist of the synthesis filter. The synthesis filter

will receive input. The excitement signal will be passed through the filter to produce the

synthesized speech signal.

(6) Reconstruct signals-In this module, the frames are put back together to reconstruct the

originalsignal.

Speech file Compression based on LPC

61

(7) Compressed Speech file-This is the final output of our system. It will be compared with

input and analyzed accordingly.

Analysis

Synthesis

Synthesis

Figure 5.2 Block diagram representation of Speech file compression based on LPC Analysis

& Synthesis

5.3 Source filter model for sound production

The source-filter model for sound production is shown in fig. 5.3. As represented in

fig.5.3 air from the lungs is taken as a source and the vocal tract as a filter. Voiced sounds

are usually vowels and often have high average energy levels and very distinct resonant or

formant frequencies. Voiced sounds are generated by air from the lungs being forced over

the vocal cords. Unvoiced sounds are usually consonants and generally have less energy and

higher frequency than voiced sounds. The production of unvoiced sounds involves air being

forced through the vocal tract in a turbulent flow. During this process the vocal cords do not

vibrate, instead, they stay open until the sound is produced. It is based on the idea of

separating the source from the filter in the production of sound.

The V.T. and nasal tract can be shown as tubes of non-uniform cross-sectional area.

As sound generated and propagates down the tubes, the frequency spectrum is shaped by the

frequency selectivity of the tube. This effect is very similar to the resonance effects observed

with organ pipe or wind instruments. In the context of speech production, resonant

frequencies of the vocal tract tube are called formant frequencies or simply formants. The

formants depend upon the shape and dimension of the vocal tract; each shape is

characterized by a set of formant frequencies. Different sounds are formed by varying the

Speech

file

Divide into

frames

Find

parameters

Apply LPC

techniques

Reconstruct

signal

Regenerate

frames

Compressed

speech file


62

shape of the V.T. Thus the spectral properties of the speech signal vary with time as the

vocal tract shape varies.

HUMAN SPEECH PRODUCTION

QUASI PERIODIC

EXCITATION SIGNAL

LUNGS

MOUTH-NOSE

VOCAL CORDS SPEECH

VOICE CODER SPEECH PRODUCTION

LUNGS VOICED/UNVOICED

DECISIION MOUTH-NOSE

FUNDAMENTAL SPEECH

FREQUENCY

FILTER

CO-EFFICIENT

ENERGY VALUE

.............................EXCITATION................................ ......ARTICULATION............

Figure 5.3 Source filter model for sound production [13]

When the tenth-order all-pole filter representing the vocal tract is excited by white

noise, the signal model corresponds to an autoregressive (AR) time-series representation.

The coefficients of the AR model can be determined using linear prediction techniques. The

application of linear prediction in speech processing, specifically in speech coding, is often

referred to as Linear Predictive Coding (LPC). The LPC parameterization is a central

component of many compression algorithms that are used in cellular telephony for

bandwidth compression and enhanced privacy. Bandwidth is conserved by reducing the data

rate required to represent the speech signal. This data rate reduction is achieved by a

parameterzing speech in terms of the AR or all-pole filter coefficients, a small set of

SOUND

PRESSURE

UNVOICED

EXCITATION

VOICED

EXCITATION

ARTICU

LATION

ENERGY

NOISE

GENERATOR

TONE

GENERATOR

VARIABLE

FILTER

Source filter model for sound production

63

excitation parameters. The two excitation parameters for the synthesis configuration shown

in fig. 5.4 are the following: (a) the voicing decision (voiced/unvoiced) and (b) the pitch

period. In the simplest case, 160 samples (20 ms for 8 kHz sampling) of speech (voice) can

be represented with ten all-pole vocal tract parameters and two parameters that specify the

excitation signal. Therefore, in this case,160 speech samples can be represented by only

twelve parameters which result in a data compression ratio of more than 13 to 1 in terms of

the number of parameters that will be encoded, transmitted. In new standardized algorithms,

more elaborate forms of parameterization exploit further redundancy in the signal, yield

better compression, much-improved speech (voice) quality [13,143].

Speech

Pitch

Period

Filter

co-efficient

Voicing Gain

Figure 5.4 The Classical Linear Prediction Coefficients (LPC) model of speech (voice)

production [13]

5.4 Linear predictive coding (LPC)

Linear predictive coding (LPC) is a widely used technique in audio signal processing,

especially in speech signal processing. It has found particular use in voice signal

compression, allowing for very high compression rates. Linear prediction (LP) forms an

integral part of almost all modern-day speech coding algorithms. The fundamental idea is

that a speech (voice) sample can be approximated as a linear combination of past samples.

Within a signal frame, the weights used to compute the linear combination are found by

minimizing the mean-squared prediction error; the resultant weights, or linear prediction

White noise

generator

Impulse train

generator Synthesis

filter

Voiced/

unvoiced

switch

All pole

representing

Vocal Tract

×


64

coefficients (LPCs), are used to represent the particular frame. LPC determines the

coefficients of a forward linear predictor by minimizing the prediction error in the least-

squares sense. It has applications in filter design and speech coding.

Linear prediction is an identification technique where parameters of a system are

found from the observation. The basic assumption is that speech can be modeled as an AR

signal, which in practice is appropriate. LP is a spectrum estimation method in the sense that

its analysis allows the computation of the AR parameters, which define the PSD of the signal

itself. By computing the LPCs of a signal frame, it is possible to generate another signal in

such a way that the spectral contents are close to the original one. LP can also be viewed as a

redundancy removal procedure where information repeated in an event is eliminated. After

all, there is no need for transmission of certain data can be predicted. By displacing the

redundancy in a signal, the number of bits required to carry the information is lowered,

therefore achieving the purpose of compression [7,13].

As shown in fig. 5.5, consider the block to be a predictor, which tries to predict the

current output as a linear combination of previous outputs(hence LPC) The predictor‟s input

is the prediction error. The parameter estimation process is repeated for each frame, with the

results representing information on the frame Thus, instead of transmitting the PCM

samples, parameters of the model are sent. By carefully allocating bits for each parameter to

minimize distortion, an impressive compression ratio can be achieved up to 50-60 times.

Here, linear prediction is described as a system identification problem, where the parameters

of an AR model are estimated from the signal itself. The situation is illustrated in fig. 5.5.

The white noise signal x[n] is filtered by the AR process synthesizer to obtain s[n]-the AR

signal-with the AR parameters denoted by âi. A linear predictor is used to predict s[n] based

on the M past samples; this is done with

M

i

insi

ans

1

][][^

........................(12)

where the ai are the estimates of the AR parameters and are referred to as the linear

prediction coefficients (LPCs). The constant M is known as the prediction order. Therefore,

the prediction is based on a linear combination of the M past samples of the signal, and

hence the prediction is linear. The prediction error is equal to

][][][ nsnsne ^...................................(13)

Fig.5.6 shows the signal flow graph implementation of the error equation and is

Linear predictive coding (LPC)

65

known as the prediction-error filter: it takes an AR signal as input to produce the prediction-

error signal at its output [13].

Error Minimization:

The system identification problem consists of the estimation of the AR parameters âi from

s[n], with the estimates being the LPCs. To perform the estimation, a criterion must be

established. In the present case, the mean-squared prediction error,

Figure 5.6 The prediction-error filter

2

1

][)(]}[{ 2M

i

insiansEneEJ

...............(14)

is minimized by selecting the appropriate LPCs. Note that the cost function J is precisely a

second-order function of the LPCs. Consequently, we may visualize the dependence of the

cost function J on the estimates a1, a2..., aM as a bowl-shaped (M+1)-dimensional surface

Figure 5.5 Linear prediction as system identification


66

with M degrees of freedom. This surface is characterized by a unique minimum. The optimal

LPCs can be found by setting the partial derivatives of J concerning ai to zero; that means

0][

1

][)(2

kns

M

i

insiansEka

J

...........(15)

for k=1,2.........M At this point, it is maintained without proof that when the above equation

is satisfied, then ai = âi; that is, the LPCs are equal to the AR parameters. Thus, when the

LPCs are found, the system used to generate the AR signal (AR process synthesizer) is

uniquely identified[13].

Prediction Gain:

The prediction gain of a predictor is given by

.........(16)

It is the ratio between the variance of the input signal and the variance of the prediction error

in decibels (dB). Prediction gain is a measure of the predictor‟s performance. A better

predictor is capable of generating lower prediction error, leading to a higher gain[13].

Minimum Mean-Squared Prediction Error:

From Fig. 5.5 we can see that when ai=âi, e[n]=x[n]; that means prediction error is

the same as the white noise used to generate the AR signal s[n]. Indeed, this is the optimal

situation where the mean-squared error is minimized, with

2][2]}[{ 2

xnxEneEJ

...........(17)

or equivalently, the prediction gain is maximized. The optimal condition can be reached

when the order of the predictor is equal to or higher than the order of the AR process

synthesizer. In practice, M is usually unknown. A simple method to estimate M from a signal

source is by plotting the prediction gain as a function of the prediction order. In this way it is

possible to determine the prediction order for which the gain saturates; that means further

increasing the prediction order from a certain critical point will not provide additional gain.

The value of the predictor order at the mentioned critical point represents a good estimate of

the order of the AR signal under consideration. The cost function J in the error minimization

equation is characterized by a unique minimum. If the prediction order M is known, J is

]}[2{

]}[2{log10

2

2log10

1010

neE

nsE

e

sPG


67

minimized when ai =âi, leading to e[n]= x[n]; that is, the prediction error is equal to the

excitation signal of the AR process synthesizer. This is a reasonable result since the best that

the prediction error filter can do is to „„whiten‟‟ the AR signal s[n]. Thus, the maximum

prediction gain is given by the ratio between the variance of s[n] and the variance of x[n] in

decibels. Taking into account the AR parameters used to generate the signal s[n], we have

M

i

isRias

Rx

J

1

][]0[2min

......................(18)

White noise is generated using a random number generator with uniform distribution

and unit variance. This signal is then filtered by an AR synthesizer with co-efficient as

indicated in table 5.1.

Table 5.1 AR synthesizer with ten LPC co-efficient

a1 = 1.534 a2 = 1 a3 = 0.587 a4 = 0.347 a5 = 0.08

a6 = -0.061 a7 = -0.172 a8 = -0.156 a9 = -0.157 a10 = -0.141

The frame of the resultant AR signal is used for LP analysis, with a length of 240

samples. Nonrecursive autocorrelation estimation using a Hamming window is applied. LP

analysis is performed with prediction order ranging from 2 to 20; prediction error and

prediction gain are found for each case. Fig. 5.7 summarizes the results, where we can see

that the prediction gain (PG) grows initially from M= 2 and is maximized when M = 10.

Further increasing the prediction order will not provide additional gain; in fact, it can even

reduce it. This is an expected result since the AR model used to generate the signal has order

ten. We observe that for the unvoiced frame, PG increases abruptly when the prediction

order goes from 2 to 5. Increasing the prediction order provides additional gain increase but

at a milder pace. For M > 10, prediction gain remains essentially constant, implying the fact

that correlation between far separated samples is low[13].

Figure 5.7 Prediction gain (PG) as a function of the prediction order (M)


68

As shown in Fig. 5.8 for the voiced frame, prediction gain is low for M˂ 3, it remains almost

constant for 4 < M < 49, and it reaches a peak for M˃ 49. The phenomenon is because, for

the voiced frame under consideration, the pitch period = 49. For M < 49, the number of

LPCs is not enough to remove the correlation between samples one pitch period apart. For

M˃ 49, however, the linear predictor is capable of modeling the correlation between samples

one pitch period apart, leading therefore to a substantial improvement in prediction gain.

Further note that the change in prediction gain is abrupt: between M = 48 and 49, for

instance, a jump of nearly 3 dB in prediction gain is observed[13].

Figure 5.8 A plot of PG as a function of the prediction order (M) for the signal frames [13]

The effectiveness of the predictor at different prediction orders can be studied further

by observing the level of „„whiteness‟‟ in the prediction-error sequence. The prediction-error

filter associated with a good predictor is capable of removing as much correlation as possible

from the signal samples, leading to a prediction error sequence with a flat PSD. The fig. 5.9

illustrates the prediction-error sequence of the unvoiced frame and the corresponding

periodogram for different prediction order. Note that M=4 is not enough to „„whiten‟‟ the

original signal frame, where we can see that the periodogram of the prediction error does not

mimic the flat spectrum of a white noise signal. For M=10, however, flatness is achieved in

the periodogram and, hence, the prediction error becomes „„roughly‟‟ white.

The fig.5.10 shows the prediction-error sequences and the corresponding

periodograms of the voiced frame. We can see that for M=3, a high level of periodicity is

still present in the prediction-error sequence and a harmonic structure is observed in the

corresponding periodogram. When M =10, the amplitude of the prediction error sequence


69

Figure 5.10 Plots of prediction error and periodograms for the voiced frame

M=3, M =10 and M= 50[13]

Figure 5.9 Plots of prediction error and period grams for the unvoiced frame

Top: M = 4 & Bottom: M =10[13]


70

becomes lower, However, the periodic components remain. As we can see, the periodogram

develops a flatter appearance, but the harmonic structure is still present. For M=50,

periodicity in the time and frequency domain is reduced to a minimum. Hence, to effectively

''whiten'' the voiced frame, a minimum prediction order 50 is required.

Fig. 5.11 compares the theoretical PSD (defined with the original AR parameters)

with the spectrum estimates found with the LPCs computed from the signal frame using M =

2, 10, and 20. For low prediction order, the resultant spectrum is not capable of fitting the

original PSD. An excessively high order, on the other hand, leads to over fitting, where

undesirable errors are introduced. In the present case, a prediction order of 10 is optimal.

Note how the spectrum of the original signal is captured by the estimated LPCs. This is the

reason why LP analysis is known as a spectrum estimation technique, specifically a

parametric spectrum estimation method since the process is done through a set of parameters

or coefficients [13].

Figure 5.11 Plot of PSD for M=2, M=10,M=20[13]

Linear Prediction and Autoregressive Modeling

71

5.5 Linear Prediction and Autoregressive Modeling

Linear Prediction Autoregressive Modeling

In the case of linear prediction, the

intention is to determine an FIR filter that

can optimally predict future samples of an

autoregressive process based on a linear

combination of past samples.

In the case of autoregressive modeling,

the intention is to determine an all-pole

IIR filter, that when excited with white

noise produces a signal with the same

statistics as the autoregressive process

that we are trying to model.

LPC returns the coefficients of the entire

whitening filter A(z), this filter takes AR

signal x as a input and returns as output

the prediction error. However, A(z) has

the prediction filter embedded in it, in the

form B(z)= 1- A(z).

Generate an AR Signal using an All-

Pole Filter with White Noise as Input

By using the LPC function and an FIR

filter simply to come up with

parameters we will use to create the

autoregressive signal

Extract B(z) from A(z) as described

above to use the FIR linear predictor

filter.

To generate the autoregressive signal,

we will excite an all-pole filter with

white Gaussian noise of variance p0.

Obtain an estimate of future values of the

autoregressive signal based on linear

combinations of past values.

To get variance p0, use SQRT(p0) as

the 'gain' term in the noise generator.

Compare Actual and Predicted Signals. Use the white Gaussian noise signal

and the all-pole filter to generate an

AR signal.

Plot 200 samples of the original AR

signal along with the signal estimate

resulting from the linear predictor.

Find AR Model from Signal using the

Yule-Walker Method.

Compare Prediction Errors. Compare AR Model with AR Signal.

The prediction error power (variance) is

returned as the second output from LPC.

overlay the power spectral density of

the output of the model, computed

using FREQZ, with the power spectral

density estimate of x, computed using

PERIODOGRAM.


72

5.6 Linear Prediction (L.P.) and Bandwidth extension (B.W.E.)

LPC analysis is used to constructing the LPC coefficients for the inverse transfer

function of the vocal tract. The standard methods for LPC coefficients estimation have the

assumption that the input signal is stationary. The quasi-stationary signal is obtained by

framing the input signal which is often done in frames in the length of 20 ms. A more

stationary signal results in a better LPC analysis because the signal is better described by the

LPC coefficients and therefore minimizes the residual signal.

5.6.1. LPC analysis

The fig. 5.12 shows a block diagram of an LPC analysis, where S is the input signal,

g is the gain of the residual signal and a is a vector containing the LPC coefficients to a

specific order. The size of the vector depends on the order of the LPC analysis. Bigger order

means more LPC coefficients and therefore better estimation of the vocal tract.

Figure 5.12 LPC analysis

5.6.2 LPC estimation

LPC estimation calculates an error signal from the LPC coefficients from LPC

analysis. This error signal is called the residual signal which could not be modeled by the

LPC analysis. This signal is calculated by filtering the original signal with the inverse

transfer function from LPC analysis. If the inverse transfer function from LPC analysis is

equal to the vocal tract transfer function then the residual signal obtained from the LPC

estimation equal to the residual signal which is put into the vocal tract. In that case, residual

signal equal to the impulses or noise from human speech production. The fig. 5.13 shows a

block diagram of LPC estimation where S is the input signal, g and a is calculated from LPC

analysis and e is the residual signal for LPC estimation.

Figure 5.13 LPC estimation

g, gwb *awb

a

Input

Signal S

LPC

analysis g, a

Input

Signal

S

LPC

estimation e

Linear Prediction (L.P.) and Bandwidth extension (B.W.E.)

73

5.6.3 LPC-synthesis

LPC synthesis is used to reconstruct a signal from the residual signal and the transfer

function of the vocal tract. Because the vocal tract transfer function is estimated from LPC

analysis can this be used combined with the residual/error signal from LPC estimation to

construct the original signal. Fig. 5.14 shows a block diagram of LPC synthesis where e is

the error signal found from LPC estimation and g and a from LPC analysis. Reconstruction

of the original signal s is done by filtering the error signal with the vocal tract transfer

function respectively. finally both the output are combined via an LPC synthesizer to get the

required W.B. signal.

Figure 5.14 LPC synthesis

Input signal before LPC analyzer, input signal after LPC synthesizer with the error signal is

depicted in fig.5.15.

Figure 5.15 Input signal before LPC analyzer, input signal after LPC synthesizer with the

error signal.

g, a

e LPC

synthesis Input

Signal S


74

Fig. 5.16 represents the complete block diagram of bandwidth extended output from band-

limited N.B. signal. The N.B. signal is processed by the LPC Estimation block to get gain

and LP Coefficient, while LPC Analyzer produced residual error signal based on two input

signals one is the N.B. signal and the other is LPC estimated output. after that LPC

estimated output is will be processed by envelope extension and LPC analyzer output

processed by Excitation Extension to get W.B. gain and LP Coefficient and W.B. residual

error signal. The more simplified version of the fig.5.16 is depicted in the fig.5.17. The basic

thought approached in the fig.5.17 is to create a signal that contains the frequencies that are

missing from the original N.B. signal. This signal having its energy mainly on the frequency

band 4–8kHz is then added to an interpolated version of the original N.B. signal that has

most of its energy on the band 0–4kHz. the extension causes a delay in the signal and so the

resampled N.B. signal must be delayed to synchronize the signals. Start the procedure by

windowing the incoming signal. The frames are decomposed into source signal and filter

parts using LP analysis and the parts are extended separately. The frame waveform is rebuilt

by filtering the extended source signal using the extended filter. After scaling the frames are

joined together using overlap-add.

Figure 5.16 Complete block diagram of B.W.E. output from band-limited N.B. signal

LPC

Estimation

Envelope

Extension

LPC

Analysis

Excitation

Extension

LPC

Synthesis

enb ewb

Swb

Snb gnb *anb gwb *awb

Bandwidth Extension based on Sub band filter and Evaluation of speech signal through

Source Filter Model

75

Figure 5.17 Bandwidth extension (B.W.E.) based on Linear Prediction (L.P.)

5.7 Bandwidth Extension based on Sub band filter and Evaluation of

speech signal through Source Filter Model

The Bandwidth extension system based on the Sub band filter is shown in fig. 5.18.

The block diagram containing high band, low band analysis sub band coder, down sampler,

G.711 encoder at transmitting terminal, G.711 decoder, up sampler, high band, low band

synthesis sub-band coder. At the user end terminal original W.B. speech signal is placed into

a sub-band filter [18]. Then filter outcomes are decimated by two. In this way, both HF and

LF segments through 8 kHz sampling frequency are acquired. Next, the LF parts are encoded

by the narrowband encoder which is G.711 encoder [19]. the HF part is transmitted without

changes at the transmitter side to the receiver through the transmission channel. At the user

end, the N.B. speech (voice) signal is decoded through the N.B. speech (voice) decoder, HF

is restored as it is. Finally the signal is synthesized by high band, low band synthesis sub-

band coder for attaining near-perfect reconstruction of the original signal at the output which

can be observed on the spectrum analyzer. Listener speech (voice) file played on the Audio

device writer.

Performance evaluation of speech (voice) signal based on the source filter model are

shown in fig. 5.19, 5.20 and 5.21 respectively. As shown in fig. 5.18 the original speech

(voice) signal is passed through the LPC analysis block to separate the LPC co-efficient,

residual component which is further processed by bit stream quantization to make a

quantized signal level that is processed by LPC synthesizer to make a compressed original

speech (voice) signal. As shown in fig. 5.19 the speech (voice) signal is broken up into

frames of size 20 ms (160 samples), with an overlap of 10 ms (80 samples). Each frame is

LP

Analysis

Source

Signal

Extension

Spectral

Envelope

Extension

Waveform

generation

Narrowband

Signal

Bandwidth

Extended

Output

Resampled and

Delayed N.B.

Signal


76

windowed using a Hamming window. 11th

order autocorrelation coefficients are initiated,

reflection coefficients are calculated from the autocorrelation coefficients using the Levinson

approach. The original speech (voice) signal is conceded via an analysis filter, which is an

all-zero filter with coefficients as the reflection coefficients obtained above. The output of

the filter is the residual signal. As shown in fig. 5.21 LPC Synthesizer is a time-varying filter

found in the receiver section of the system. It reconstructs the original signal using the

reflection coefficients, residual signal. Speech file is played through the audio player.

Fig. 5.22 & 5.23 shows the input and output time-domain waveform of the wave file

''OM SHRI GANESHAY NAMAH'', while fig. 5.24 & 5.25 the input, output frequency

domain waveform of the wave file ''OM SHRI GANESHAY NAMAH''. From fig. 5.22,

5.23, 5.24, 5.25, it is identified that the signal looks very different in the time domain but in

the frequency domain, it looks like same because of the nearly same power distribution at the

same frequency will be observed which can be heard on the audio player. The result

displayed on each stage by using a spectrum analyzer or time scope or display is taking some

time because in our simulation 1536 samples need to be updated for displaying result at each

stage once the required number of samples are acquired there is no issue regarding results.

one other noticeable remark from viewing various time/frequency domain waveform is that

at each stage of the source-filter model approach the sampling frequency is changing so

variation in the signal waveform can be obtained. Pre-Emphasized Speech signal, hamming

windowed Speech signal, LPC analyzer output, LPC synthesizer output, auto cor-relation co-

efficient, reflection coefficient(RC), and LPC co-efficient are represented in fig. 5.26 to 5.29

respectively. From the obtained RC co-efficient in fig. 5.30 ,one can say that the value of

RC co-efficient <1 is obtained for the given input speech file to ensure stability for Linear

prediction (L.P.) which can be discussed further in the next section.

Figure 5.18 Bandwidth extension based on Sub band filter


Source Filter Model

77

Figure 5.19 Performance evaluation of speech signal based on Source filter

Figure 5.20 Source Filter model-based Analysis

Figure 5.21 Source Filter model-based Synthesis


78

Figure 5.22 I/P time-domain waveform of wave file ''OM SHRI GANESHAY NAMAH''

Figure 5.23 O/P time-domain waveform of wave file "OM SHRI GANESHAY NAMAH"

Figure 2 Input frequency domain waveform of wave file "OM SHRI GANESHAY

NAMAH"

Figure 5.24 Input frequency domain waveform of Wave file "OM SHRI GANESHAY

NAMAH"


Source Filter Model

79

Figure 5.25 O/P freq. domain waveform of Wave file "OM SHRI GANESHAY NAMAH"

Figure 5.26 Pre-Emphasized Speech signal

Figure 3 A hamming windowed Speech signal

Figure 5.27 A hamming windowed Speech signal


80

Figure 5.28 LPC analyzer output

Figure 5.29 LPC synthesizer output

5.8 Stability consideration (LP)

The prediction-error filter with system function

n

k

kk za

1

1A(z) ..............(19)

where the ai are the LPCs found by solving the normal equation, is a minimum phase system

if and only if the associated RCs ki satisfy the condition.

Mi .......2,1;1ik ..........(20)

The fact that A(z) represents a minimum phase system implies that the zeros of A(z) are

inside the unit circle of the z-plane. Thus, the poles of the inverse system 1/ A(z) are also

Stability consideration (LP)

81

inside the unit circle. Hence, the inverse system is guaranteed to be stable if the RCs satisfy

the above equation. Condition Since the inverse system is used to synthesize the output

signal in an LP-based speech coding algorithm, stability is mandatory with all the poles

located inside the unit circle.

Figure 5.30 Auto cor-relation co-efficient, Reflection co-efficient & LPC co-efficient

For real speech data prediction order must be high enough to include at least one

pitch period to model adequately the voiced signal under consideration. A linear predictor

with an order of ten is not capable of accurately modeling the periodicity of the voiced signal

having a pitch period of 50. The problem is evident when the prediction error is examined: a

lack of fit is indicated by the remaining periodic component. By increasing the prediction

order to include one pitch period, the periodicity in the prediction error has largely

disappeared, leading to a rise in prediction gain. High prediction order leads to excessive bit-

rate and implementation cost since more bits are required to represent the LPCs, and extra

computation is needed during analysis. Thus, it is desirable to come up with a scheme that is

simple and yet able to model the signal with sufficient accuracy[13].

An increase in prediction gain is due mainly to the first 8 to 10 coefficients, plus the

coefficient at the pitch period, equal to 49 in that particular case. The LPCs at orders between

11 and 48 and at orders greater than 49 provide essentially no contribution toward improving

the prediction gain. This can be seen from the flat segments from 10 to 49, and beyond 50.

Therefore coefficients that are not contributing toward elevating the prediction gain can be


82

eliminated, leading to a more compact and efficient scheme. In long-term LP short-term

predictor is connected in cascade with a long-term predictor as shown in fig.5.31. The short-

term predictor has relatively low prediction order M in the range of 8 to 12. This predictor

eliminates the correlation between nearby samples or is short-term in the temporal sense. The

long-term predictor targets the correlation between samples one pitch period apart.

Figure 5.31 Short-term prediction-error filter connected in series to a long-term prediction-

error filter

The long-term prediction-error filter with input es[n] and output e[n] has system function

T-bz 1 H(z)

.......................(21)

two parameters are required to specify the filter: pitch period T and long term gain b (also

known as long-term LPC or pitch gain).

The fig.5.32 shows the block diagram of the synthesis filter, where a unit-variance

white noise is generated and scaled by the gain g and is input to the synthesis filter to

generate the synthesized speech at the output. Since x[n] has unit variance, g[n] has variance

equal to g2.

Figure 5.32 Block diagram of the synthesis filter

Stability consideration (LP)

83

M

i

iRa si

1

][ [0]R sg ........................(22)

Thus, the gain can be found by knowing the LPCs and the autocorrelation values of the

original signal. In the above equation, g is a scaling constant. A scaling constant is needed

because the autocorrelation values are normally estimated using a window that weakens the

signal‟s power. The value of g depends on the type of window selected and can be found

experimentally. Typical values of g range from 1 to 2. Also, it is important to note that the

autocorrelation values in the equation must be the time-averaged ones, instead of merely the

sum of products[13].

The long-term predictor is responsible for generating a correlation between samples

that are one pitch period apart. The filter with system function

T-bz 1

1 (z)HP

........................................ (23)

describing the effect of the long-term predictor in synthesis is known as the long-term

synthesis filter or pitch synthesis filter. On the other hand, the short-term predictor recreates

the correlation present between nearby samples, with a typical prediction order equal to ten.

The synthesis filter associated with the short-term predictor, with system function given by

equation is also known as the formant synthesis filter since it generates the envelope of the

spectrum in a way similar to the vocal track tube, with resonant frequencies known simply as

formants. For the pitch synthesis filter with system function (4.89), the system poles are

found by solving

0bz 1T- ................................ (24)

bzT-

............................................ (25)

So we can say that there are T different solutions for Z, the system has T different poles.

For Long term LP analysis

n s

n s

Tne

Tnesneb

][

][][2

............. (26)

There are a total of T different solutions for z, and hence the system has T different

poles. These poles lie at the vertices of a regular polygon of T sides that is inscribed in a

circle of radius |b|1/T

. Thus, for the filter to be stable, the following condition must be

satisfied:


84

1 b

....................................................................... (27)

An unstable pitch synthesis filter arises when the absolute value of the numerator is

greater than the denominator as in (4.84), resulting in |b| > 1. This usually arises when a

transition from an unvoiced to a voiced segment takes place and is marked by a rapid surge

in signal energy. When processing a voiced frame that occurs just after an unvoiced frame,

the denominator quantity Σes2[n-T] involves the sum of the squares of amplitudes in the

unvoiced segment, which is normally weak. On the other hand, the numerator quantity

Σes[n]*es[n-T] involves the sum of the products of the higher amplitudes from the voiced

frame and the lower amplitudes from the unvoiced frame. Under these circumstances, the

numerator can be larger in magnitude than the denominator, leading to |b| > 1. To ensure

stability, the long-term gain is often truncated so that its magnitude is always less than

one[13].

Subjective & Objective Measurement for W.B. to S.W.B.

85

CHAPTER 6

Comparative Analysis of Proposed Model with

Baseline & Next-Generation Speech Codec

6.1 Subjective & Objective Measurement for W.B. to S.W.B.

For quality measurement which is highly subjective evaluation, comparison based

mean-opinion score (CMOS) ratings are performed on various speech files [28,41]. In each

examination bandwidth, extended signals are compared with XEVS ,XH.F.B.E. Each

examination was carried out by 56 listeners, among them 28 were male, 28 were female

Speakers. They were requested to judge against the superiority of 13 randomly ordered pairs

of speech (voice) signals X and Y. Either X or Y can be processed with the proposed

algorithm, remaining by baseline or next-generation coder. The selected wave files were

judged by the individual listener and give a rating to the selected wave files in the range of -3

to 3 (7 Scale) where -3 is much worse and 3 is much better. zero ratings meaning both are of

the same quality.-2 & -1 for slightly worse and worse while 1 & 2 rating means better and

slightly better. The age group selected for the above measurement is in between 21 years to

50 years.

As shown in Table 6.1 each Listener has given the rating in the scale of -3 to 3 for

Each of the 13 wave files and finally, the average value of all listeners is calculated to

rate/judge the CMOS Score of the proposed algorithm and baseline/next generation coder

algorithm. here the table 6.1 is prepared for the proposed coder, the same way table can be

prepared for baseline/next generation coder algorithm. The samples were played using

Logitech good quality headphones. Speech files used for quality measurement are available

online. For intelligibly highly objective measurement PESQ can be characterized as

.....................(28)

where x is the raw P.E.S.Q. output (ITU P.862.2). One can use the above expression to

obtain an equivalent PESQ score for the M.O.S.L.Q. score (=y) as discussed in subjective

measurement for W.B. to S.W.B.. Below expression can be used to get P.E.S.Q. output from

8224.3*3669.11

999.0999.4999.0

xey


86

M.O.S.L.Q. score [41].

.............................(29)

Table 6.1 Listener rating in a scale of -3 to 3 for different wave files

Speech

file

MA0

1_

01

MA0

1_02

MA0

1_03

MA0

1_04

MA0

1_05

FA01

_01

FA01

_03

FA0

1_04

FA0

1_05

arctic

_a000

2

arcti

c_a0

003

arcti

c_a0

004

arcti

c_a0

002

L

i

s

t

n

e

n

e

r

r

a

t

i

n

g

i

n

a

s

c

a

l

e

o

f

-

3

t

o

3

L-1 (M)

2 2 2 2 2 2 2 2 1 2 1 1 3

L-2

(M)

2 3 3 2 2 0 1 1 0 1 2 2 2

L-3 (M)

2 1 1 2 1 2 2 1 1 2 1 2 3

L-4

(M)

1 2 2 1 1 1 1 1 1 1 0 1 1

L-5 (M)

2 2 1 2 2 2 2 2 1 1 2 2 2

L-6

(M)

1 3 2 1 3 1 1 1 1 1 0 1 2

L-7 (M)

0 1 2 2 2 2 1 2 0 2 2 2 1

L-8

(M)

2 1 2 2 1 1 1 1 1 1 1 1 2

L-9 (M)

2 3 1 2 2 0 0 2 2 1 2 2 2

L-10

M)

1 2 0 1 2 2 1 1 1 2 2 1 2

L-11 (M)

2 2 2 2 3 1 1 0 0 2 1 0 2

L-12

(M)

2 2 1 2 2 1 0 1 1 2 2 2 1

L-13(M)

1 1 2 1 1 1 1 2 2 1 1 2 2

L-

14(M)

1 3 2 2 3 0 0 1 1 2 2 2 2

L-

15(M)

1 2 3 1 2 0 2 2 1 0 2 1 3

L-

16(M)

1 3 2 2 3 2 1 0 1 1 1 1 2

L-

17(M)

2 1 3 1 1 2 2 2 0 1 1 0 2

L-

18(M)

1 2 1 0 2 1 1 1 1 2 1 2 3

L-

19(M)

2 3 2 2 1 2 2 1 1 2 2 1 2

L-

20(M)

2 2 2 2 2 1 1 1 2 0 1 2 1

L-

21(M)

2 3 1 2 3 0 2 1 1 2 2 1 2

L-

22(M)

2 2 2 1 2 2 0 1 2 1 2 2 1

L-

23(M)

1 1 2 2 1 1 2 0 1 1 1 1 2

L-

24(M)

2 0 2 2 0 0 1 1 1 0 2 1 2

L-

25(M)

1 2 2 1 2 1 2 1 1 2 1 2 1

L-26(M)

1 1 1 2 1 1 2 0 2 1 2 2 1

L-

27(M)

1 3 2 1 3 2 1 1 1 2 1 2 2

L-28(M)

2 2 2 2 2 1 2 0 2 1 2 2 1

L-1 (F) 1 3 3 1 3 1 1 0 2 0 0 2 2

L-2 (F) 2 2 2 1 2 2 1 2 1 2 2 1 3

4945.1

999.0

999.4ln6607.4

y

y

x


87

L-3 (F) 2 3 2 0 3 1 0 2 2 2 1 2 1

L-4 (F) 2 1 3 2 1 1 1 1 1 2 2 1 2

L-5 (F) 2 3 2 1 2 0 1 2 2 2 2 2 1

L-6 (F) 1 2 1 2 2 1 0 1 1 1 1 2 2

L-7 (F) 2 1 2 1 1 0 1 0 1 2 2 2 2

L-8 (F) 2 2 1 2 2 2 2 1 2 1 0 2 1

L-9 (F) 1 2 2 1 2 2 1 1 1 1 2 1 1

L-

10(F)

1 3 2 1 3 1 1 0 2 2 1 2 0

L-11(F)

2 2 1 2 2 1 1 1 1 1 1 1 2

L-

12(F)

2 2 2 2 1 2 2 1 2 2 2 2 1

L-13(F)

2 3 2 2 3 1 1 0 1 2 1 1 2

L-

14(F)

2 1 1 2 1 2 2 1 2 1 0 2 2

L-15(F)

1 3 1 2 3 1 0 2 0 2 1 2 1

L-

16(F)

1 2 2 1 2 2 2 1 2 2 2 2 2

L-17(F)

1 3 1 2 2 2 2 2 1 1 1 1 1

L-

18(F)

2 3 0 1 3 0 1 1 2 2 0 2 0

L-19(F)

2 2 2 2 2 1 2 2 2 2 1 2 2

L-

20(F)

0 3 1 2 3 2 1 1 1 1 2 1 1

L-21(F)

2 2 2 2 2 0 0 2 2 1 1 2 2

L-

22(F)

1 3 1 2 1 2 1 0 0 1 1 2 1

L-

23(F)

1 2 2 1 2 1 2 2 2 2 0 2 2

L-

24(F)

0 3 3 2 3 0 1 1 2 2 1 2 3

L-25(F)

2 1 1 1 1 2 0 2 1 1 1 1 1

L-

26(F)

1 3 2 2 3 1 1 2 2 2 1 2 2

L-27(F)

2 2 1 1 2 0 2 1 1 1 1 1 1

L-

28(F)

1 3 2 2 1 1 1 2 0 0 0 2 2

=1.48 =2.13 =1.74 =1.56 =1.96 =1.14 =1.18 1.15 1.23 1.38 1.24 1.57 1.70

From the MATLAB based simulation results obtained through programming in

MATLAB, one can observe that in terms of objective measurement (P.E.S.Q.) the proposed

method based on LP comparable results is achieved in comparison with the baseline

algorithm, next-generation super wideband E.V.S. coder as indicated in Table 6.3 (fig. 6.2)

& Table 6.5 (fig. 6.4). In terms of subjective measurement (mean opinion score) the result of

the proposed coder is comparable in comparison with the baseline algorithm and next-

generation Super wideband E.V.S. coder as seen in Table 6.2 (fig. 6.1) and Table 6.4 (fig.

6.3).The Comparative analysis of all algorithms for various wave files has been carried out.

From the results, it can be concluded that the result of the proposed coder is comparable in

comparison with the baseline algorithm and next-generation Super wideband E.V.S. coder.

As indicated in Table 6.6 (fig. 6.5), Table 6.7 (fig.6.6) , Table 6.8 (fig.6.7) to observe the


88

effect of energy on speech (voice) quality subjective measurement (CMOS) has been carried

out by taking variation in gain for different prediction order and it is found that baseline

H.F.B.E. algorithm is dependent on variation in gain, No change in prediction order affect

result. For next generation S.W.B. coder variation of gain and prediction order not affect the

obtained result means steady results for all prediction order and change in gain. For Proposed

algorithm for unity gain not much variation is observed for various prediction order, so

prediction order of 16 (LP) with unity gain is considered as a standard at various stages in

the thesis due to accurate representation and complexity problem. By observing the

spectrogram of input and output speech files for baseline, proposed as well as a next-

generation coder and by comparing all the performed results one can say that in the

bandwidth-limited input signal(N.B./W.B.) the required spectral component is absent due to

missing H.B. information and the result obtained by proposed coder are comparable in

comparison with baseline algorithm and next-generation super wideband E.V.S. coder as

shown in fig. 6.8 to 6.20.

Table 6.2: C.M.O.S.-L.Q.O.for proposed, H.F.B.E., LP_order=12,16,24 & E.V.S. algorithm for

bdl_arctic_a0001.wav

File name proposed

algorithm

(CMOS-

L.Q.O.)

(LP-12)

proposed

Algorithm

(CMOS-

L.Q.O.)

(LP-16)

proposed

Algorithm

(CMOS-

L.Q.O.)

(LP-24)

HFBE

algorithm

(CMOS-

L.Q.O.)

EVS

algorithm

(CMOS-

L.Q.O.)

bdl_arctic_a0001.wav 1.117 1.116 1.114 1.123 1.138

Figure 6.1 C.M.O.S.-L.Q.O. for proposed, H.F.B.E., L.P._order=12,16,24 & E.V.S. algorithm

for bdl_arctic_a0001.wav


89

Table 6.3: P.E.S.Q. for proposed, H.F.B.E., LP_order=12,16,24 & E.V.S. algorithm for bdl_arctic_a0001.wav

Figure 6.2 P.E.S.Q. for proposed, H.F.B.E., LP_order=12,16,24 & E.V.S. algorithm for

bdl_arctic_a0001.wav Table 6.4: C.M.O.S.-L.Q.O. for proposed, HFBE, LP_order=12,16,24 & EVS algorithm for 13 different

speech files

Complementary Mean Opinion Score

Speech Files

Proposed

Algorithm

(LP_12)

Proposed

Algorithm

(LP_16)

Proposed

Algorithm

(LP_24)

HFBE

Algorithm

(Baseline)

EVS

Algorithm

MA01_01 1.469 1.477 1.473 1.816 4.644

MA01_02 2.116 2.128 2.123 2.573 4.644

MA01_03 1.726 1.739 1.729 2.189 4.644

MA01_04 1.560 1.562 1.558 1.992 4.644

MA01_05 1.926 1.959 1.953 2.388 4.644

FA01_01 1.143 1.145 1.142 1.223 4.644

FA01_03 1.186 1.189 1.182 1.287 4.644

FA01_04 1.145 1.15 1.142 1.241 4.644

FA01_05 1.234 1.239 1.231 1.365 4.644

arctic_a0002 1.369 1.379 1.37 1.606 4.644

arctic_a0003 1.24 1.242 1.233 1.37 4.644

arctic_a0004 1.552 1.568 1.562 1.834 4.644

arctic_a0005 1.696 1.7 1.69 1.964 4.644

file name proposed

algorithm

(P.E.S.Q.)

(LP-12)

proposed

algorithm

(P.E.S.Q.)

(LP-16)

proposed

algorithm

(P.E.S.Q.)

(LP-24)

HFBE

algorithm

(P.E.S.Q.)

EVS

algorithm

(P.E.S.Q.)

bdl_arctic_a0001.wav 0.238 0.236 0.236 0.28 0.368


90

Figure 6.3 C.M.O.S.-L.Q.O. for proposed, HFBE, LP_order=12,16,24 & EVS algorithm for

13 different speech files Table 6.5: P.E.S.Q. for proposed, HFBE, LP_order=12,16,24 & EVS algorithm for 13 different speech files

PESQ

Speech Files

Proposed

Algorithm

(LP_12)

Proposed

Algorithm

(LP_16)

Proposed

Algorithm

(LP_24)

HFBE

Algorithm

EVS

Algorithm

MA01_01 1.335 1.340 1.336 1.8 1.094

MA01_02 2.114 2.125 2.121 2.48 1.094

MA01_03 1.712 1.721 1.718 2.17 1.094

MA01_04 1.509 1.528 1.524 1.991 1.094

MA01_05 1.913 1.925 1.918 2.34 1.094

FA01_01 1.126 1.128 1.123 0.73 1.094

FA01_03 0.165 0.17 0.164 0.92 1.094

FA01_04 1.138 1.142 1.139 0.78 1.094

FA01_05 0.77 0.78 0.772 1.114 1.094

arctic_a0002 1.128 1.137 1.131 1.54 1.094

arctic_a0003 0.78 0.79 0.782 1.138 1.094

arctic_a0004 1.46 1.48 1.470 1.7 1.094

arctic_a0005 1.66 1.66 1.63 1.958 1.094


91

Table 6.6: C.M.O.S.L.Q. for proposed, HFBE, LP_order=24 & EVS algorithm for various gain for wave file




LP_order_wb = 16 & Gain= 0.50, 0.75 & 1

Complementary Mean Opinion Score for wave file bdl_arctic_a0001.wav

LPAS

(Proposed)

LP_order=

16

Gain =0.5

LPAS

(Propos

ed)

LP_ord

er=16

Gain

=0.75

LPAS

(Propos

ed)

LP_orde

r=16

Gain =1

(HFBE

_Base

line)

Gain

=0.5

(HFBE

_Base

line)

Gain

=0.75

(HFB

E_Bas

e line)

Gain

=1

EVS

Gain

=0.5

EVS

Gain

=0.75

EVS

Gain =1

1.121

1.116

1.114

1.132

1.127

1.123

1.138

1.138

1.138



LP_order_wb = 12 & Gain= 0.50, 0.75 & 1


LPAS

(Proposed)

LP_order=

12

Gain =0.5

LPAS

(Proposed)

LP_order=

12

Gain =0.75

LPAS

(Proposed)

LP_order=

12

Gain =1

(HFBE

_Base

line)

Gain

=0.5

(HFBE_Ba

se line)

Gain =0.75

(HFBE_

Base

line)

Gain =1

EVS

Gain

=0.5

EVS

Gain

=0.75

EVS

Gain

=1

1.128

1.123

1.115

1.132

1.127

1.123

1.138

1.138

1.138

LP_order_wb = 24 & Gain= 0.50, 0.75 & 1


LPAS

(Propo

sed)

LP_or

der=24

Gain

=0.5

LPAS

(Proposed)

LP_order=

24

Gain =0.75

LPAS

(Propos

ed)

LP_ord

er=24

Gain =1

(HFBE_Ba

se line)

Gain =0.5

(HFBE_B

ase line)

Gain

=0.75

(HFBE_

Base

line)

Gain =1

EVS

Gain

=0.5

EVS

Gain

=0.75

EVS

Gain =1

1.126

1.119

1.114

1.132

1.127

1.123

1.138

1.138

1.138


92

Figure 6.4 P.E.S.Q. for proposed, HFBE, LP_order=12,16,24 & EVS algorithm for 13

different speech files

Figure 6.5 C.M.O.S.L.Q. for proposed, HFBE, LP_order=24 & EVS algorithm for various

gain for wave file bdl_arctic_a0001.wav



Spectrogram

93



6.2 Spectrogram

A sound spectrogram is a visual representation of sound. A spectrogram provides

more complete, precise information because it is based on actual measurements of the

changing frequency content of a sound over time. The horizontal dimension of the

spectrogram corresponds to time, and the vertical dimension corresponds to frequency or

pitch, with higher sounds , shown higher on the display. The relative intensity of the sound at

any particular time and frequency is indicated by the color of the spectrogram at that point.

Spectrograms are usually created in either of two ways: approximated as a filter bank that

results from a series of band pass filters, or calculated from the time signal using the short-

time Fourier transform (STFT) when applied to an audio signal, spectrograms are sometimes

called sonograph, voiceprints, or voice grams.

Figure 6.8 Spectrogram for speech file MA01_01.wav

https://en.wikipedia.org/wiki/Audio_signal


94


Figure 4 Spectrogram for speech file MA01_02.wav

Figure 5 Spectrogram for speech file MA01_03.wav


Spectrogram

95




96

Figure 6.13 Spectrogram for speech file FA01_01.wav


Spectrogram

97




98

Figure 6.17 Spectrogram for speech file arctic-a0002.wav


Spectrogram

99




100

CHAPTER-7

Conclusion, Major Contribution, and Future Scope

Conclusion

In this research, an approach to B.W.E. lying on S.F.M. (L.P.) for N.B. to W.B. ,W.B. to

S.W.B. are discussed with measurement based on Mean Opinion Score (Subjective) and

Perceptual Evaluation of Speech Quality (P.E.S.Q.).

When only narrowband speech transmission is available, B.W.E. can be applied to the

signal at the receiving end of the communication link. B.W.E. method aims to improve

speech quality, intelligibility by regenerating new frequency components in the signal in

frequencies above 3400 Hz. Since the extension is made without any transmitted

information the algorithm is suitable for any telephone application that can reproduce

wideband audio signals.

By performing subjective, objective measurement for various speech (voice) files,

comparing the spectrogram of estimated S.W.B. with the baseline algorithm & next-

generation super wideband (E.V.S.) coder algorithm one can say that the results obtained

are comparable to the next-generation super wideband (E.V.S.) coder algorithm making

them an attractive alternative to conventional speech coder.

The subjective, objective measurement adopted here indicates that the baseline

algorithm, adopted proposed method is tremendously well-organized with minor latency.

By observing the spectrogram of input, output speech files for baseline, proposed, next-

generation coder and comparing all the performed results - one can say that in the

bandwidth-limited input signal (N.B./W.B.) the required spectral component is absent

due to missing H.B. information and the result obtained by proposed coder are

comparable in comparison with baseline algorithm, next-generation Super wideband

E.V.S. coder.

Being codec neutral, the proposed algorithm can be used to improve the speech (voice)

quality offered by wideband networks, devices. It can also be used to preserve quality

when super-wideband devices are used alongside wideband services. When used in

combination with a wideband codec which operates on some form of linear prediction

Future Scope

101

coefficients, the proposed approach avoids an additional resynthesis step to obtain super-

wide bandwidth signals, thereby reducing the complexity.

Major Contribution

The key contributions reported in thesis involve the development of an especially

efficient approach to S.W.B. B.W.E. based on linear prediction analysis-synthesis which

avoids the statistical estimation of missing higher frequency components.

In addition to computational efficiency, the solution delivers a speech (voice) of superior

quality to wideband speech signals processed with an adaptive-multi-rate wideband

codec and comparable quality attainable by super wideband E.V.S. coder.

MATLAB based simulation of sub band filter processed via source filter model are

performed. Also LPC based analysis, synthesis was carried out and result obtained are

compared in terms of spectrogram at input and output.

LPC stability is examined by checking the value of reflection co-efficient for stability

purpose.

Future Scope

In discussing directions of research, it is not viable to be exhaustive, and in predicting

what the successful directions maybe, we do not necessarily expect to be accurate.

Nevertheless, it may be useful to set down some broad research directions, with a range

that covers the obvious as well as the speculative.

The research work in this thesis may help to shed light on the proposed S.F.M. based

algorithm which can be an alternative for the next generation super wideband E.V.S.

coder employed for speech quality, intelligibility improvement.

S.W.B. to F.B. speech quality is also the next targeted area of work for researchers found

interest in the area of speech coding and processing. One can propose the model based on

the source-filter model for attaining the F.B. signal from the S.W.B. signal. The proposed

work can also be extended for music signals and music and mixed (speech and music).

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List of Publications

113

List of Publications

“Quantization & Speech Coding for Wireless Communication”, R.N.Rathod, Dr.

M.S.Holia, Kalpa Publications in Engineering,ICRISET2017, International

Conference on Research and Innovations in Science, Engineering & Technology.

Selected Papers in Engineering, volume 1, pp.19-25,2017

“Performance Analysis of Speech Coder for Wireless Communication under Varying

Channel Condition” R.N. Rathod, Dr. M.S. Holia, International Journal of

Innovations & Advancement in Computer Science IJIACS ISSN 2347-8616, UGC

Approved Journal, Journal, Impact factor 2.65, volume7, Issue-2 February 2018.

“Bandwidth Extension and Quality Evaluation of speech signal based on QMF and

source filter model using simulink and MATLAB”, R.N.Rathod, Dr. M.S.Holia,

N.S.Bhatt, International Journal of Research and Analytical Reviews,ISSN2348-

1269,UGC Approved Journal, Impact factor 5.75 Volume-6,Issue-1,Jan-March-

2019.

"Bandwidth Extension of Speech Signal Artificially & Transmission & Coding for

Wireless Communication", R. N. Rathod, M. S. Holia, Journal of Communication

Engineering & Systems, SJIF: 6.144 , Volume- 9, Issue-2, 2019.

"Analytical Studies Relating to Bandwidth Extension from Wideband to Super

wideband for Next Generation Wireless Communication", R.N.Rathod, Dr.

M.S.Holia, paper has been submitted, presented in International Conference on

Research and Innovations in Science, Engineering & Technology (ICRISET2020)

held on 4-5 September-2020 & Published in SCOPUS Indexed UGC CARE

Journal RT&A, Special Issue № 1 (60) Volume 16,Pp:206-22 January 2021.

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