23Speech v104 Vg

7/30/2019 23Speech v104 Vg

1/70

DSP C5000

Chapter 23

Mobile CommunicationSpeech Coders

Copyright 2003 Texas Instruments. All rights reserved.

7/30/2019 23Speech v104 Vg

2/70

Copyright 2003 Texas Instruments. All rights reserved.ESIEE, Slide 2

Outline

Speech Coding, CELP Coders

Implementation using C54x

7/30/2019 23Speech v104 Vg

3/70


OutlineSpeech Coding

Generalities on speech and coding

Linear Prediction based coders

Short term and long term prediction

Vector Quantization

CELP coders

Structure and calculations

Standards

7/30/2019 23Speech v104 Vg

4/70


Applications of Speech Coding

Digital Transmissions

On wired telephone:

Multiplexing

Integration of services

On wireless channels:

Spectral efficiency

For better protection against errors

Voice mail/messaging Storage: telephone answering machine

Secure phone

7/30/2019 23Speech v104 Vg

5/70


Characteristics of Coders

Bit Rate D: 50 bps < D < 96 kbps

Coding Delay ~ frame delay

Quality

Objective measurements: SNR, PSQM

Subjective measurements: MOS(excellent,good,fair,poor,unacceptable)

Intelligibility:

Objective measure STI or subjective DRT

Acceptability: E model of ETSI standard,communicability

Immunity to noise

Complexity

7/30/2019 23Speech v104 Vg

6/70


Objective Evaluation of the Quality

The PSQM method:

Objective evaluation

Based on a model of auditive perception

Takes into account the masking effects

Good correlation with the MOS grade in basic conditions:

Low bit rate speech coding, tandem, transmissionerrors, ...

But sometimes not very reliable :

Loss of frames, effect of the automatic control

Still under development (PSQM+)

7/30/2019 23Speech v104 Vg

7/70Copyright 2003 Texas Instruments. All rights reserved.ESIEE, Slide 7

Subjective Evaluation of Qualityusing the ACR Method yielding MOS score

A great number of auditors give grades to agreat number of speech sequences.

Database with phonetically balanced sentences

Presentation in random order

Naive auditors

Statistical processing of results gives the MOS.

MOS = Mean OpinionScore

Bad

Mediocre

Average

Good

Excellent

1

2

3

4

5

7/30/2019 23Speech v104 Vg


Speech Production

Diaphragm

Lungs Larynx

Vocal chords

Nasal Cavity

Mouth

Palate

Tongue

Lips

Jaws

Vocal tract

Wind Pipe

Art icu lators

Wind Tunnel

Osci l lator

7/30/2019 23Speech v104 Vg


Speech Signal

7/30/2019 23Speech v104 Vg


Speech Spectrum for a Voiced Sound

7/30/2019 23Speech v104 Vg


Speech Spectrogram

Non stationary

Voiced / unvoiced

7/30/2019 23Speech v104 Vg


Calculation of Spectrograms

Preac = Preaccentuation, enhances high freqeuncies

Window = limits the edge effects

Preac Window FFT Log(| |)

Powerspectral

density

Speech

signal

Time

Example of window

7/30/2019 23Speech v104 Vg


Example: Time Signal and Spectrogram

1 2 3 4 5 6 7 80

500

1000

1500

2000

2500

3000

3500

0 1 2 3 4 5 6 7 8 9-3

-2

-1

0

1

2

3x 10

Time

Frequency

Time

SPECTROGRAM

TIME SIGNAL

7/30/2019 23Speech v104 Vg


Equivalent Electrical Model

NV

V

Stochastic

excitation

Periodical

excitation

Speech

Spectrum

shaping filter

f

Transfer functionof the vocal tract

Voicing

Gain

7/30/2019 23Speech v104 Vg


Simplified Speech Production Model

y(t)=h(t)*e(t) - Y(z)= H(z)E(z)

V z e z e zc jb T

k

Kc jb T k k k k ( ) / ( )( )( ) ( )

1 1 11

1

1

L z z( )

1

1

G z e z cT( ) / ( ) 1 1 1 2

G(z) V(z) L(z)

glott isradiation

at the l ips

vocal

tract yy((tt))ee((tt))

7/30/2019 23Speech v104 Vg


All Pole Model of theSpectrum Shaping Filter

The filter H(z) represents the spectralenvelope since the excitation has a whitespectrum.

1

1 1( ) ( ) ( ) ( ) .

( )1

pi

i

i

H z G z V z L z A z

a z

7/30/2019 23Speech v104 Vg


Short Term Linear Prediction

The coefficients of H(z)=1/A(z) can be

obtained by linear prediction. Short term analysis on x(n)speech signal

Frames of 10 to 30 ms.

Least square error criterion:

1

2

( ) ( ) : linear prediction.

( ) ( ) ( ). criterion: min ( ).

p

i

i

n

x n a x n i

e n x n x n e n

7/30/2019 23Speech v104 Vg


Determination of the Spectral Envelope byLinear Prediction

Prediction error e(n) = residual is nearlywhite,

so the spectral envelope of x(n) can beapproximated by Sx(f):

2

2

2

( ) ,( )

is the least square prediction error.

xS fA f

7/30/2019 23Speech v104 Vg


Calculation of the Prediction Coeffcients

The prediction coefficients aiare the

solution of the normal equations:

1,1 1, 1 0,1

,1 , 0,

,

.

( ) ( ).

p

p p p p p

i j

r r a r

r r a r

r x n i x n j

The Levinson Durbin algorithm is often

used to solve these equations

7/30/2019 23Speech v104 Vg


Example of Linear Prediction

Amplitude ofthe speechsignal

Amplitude ofresidual signal

E l f Li P di ti S t l

7/30/2019 23Speech v104 Vg


Example of Linear Prediction: SpectralEnvelope Estimation

Formants

7/30/2019 23Speech v104 Vg


Estimation of the Pitch Period

Pitch Period T0 estimated by correlation

of the speech signal or residual. Other methods exist (e.g. cepstrum)

F0 = fundamental frequency = 1/T0

Fractional pitch estimation if the precisionis better than the sampling period.

0

0

50 400

120 160 f 8000 .S

S

Hz F Hz

T kT k i Fs HzT

7/30/2019 23Speech v104 Vg

23/70


Long Term Prediction (LTP)

The idea is to predict one period of

signal from the preceding one:

( ) ( ).x n b x n M

2 unknowns: b and M.

M is the pitch period (when voiced).

Least square error criterion is used.

7/30/2019 23Speech v104 Vg

24/70


Long Term Prediction (LTP)

2

n n M n M

n n

b x x x

2

2

n Mn n M

n n

x x x

For a given value of M, optimal b is:

The best M value maximizes:

All possible values of M must be tested.

7/30/2019 23Speech v104 Vg

25/70


Example of Long Term Prediction

7/30/2019 23Speech v104 Vg

26/70


LPC 10 Vocoder

One of the oldest speech coder is the

LPC10 vocoder:

The analysis (coder) calculates eachframe:

Pitch period, predictioncoefficients, energy, voicing.

The synthesis (decoder) uses theseparameters to synthesize speech fromthe electrical equivalent model.

7/30/2019 23Speech v104 Vg

27/70


LPC 10 Vocoder (Order 10)

Speech

frameLinear

Predict ion

pitch/voicing

energy

mux

(ai)

E

excitat ion

(600 bps )

Spectrum

sha ping fi lter(1800 bps )

V/UV,F

CODER

V/UV,F0

E(ai)

1/A(z)Gain

Synthet icspeech

Decoder

Frame= 22,5 ms

i i S

7/30/2019 23Speech v104 Vg

28/70


Prediction Spectral Parameters The aicoefficients are sensitive to coding and

interpolation.

They are replaced by other coefficients:

Reflexion coefficients ki, log area ratio LARi.

Line spectrum frequencies LSFi.

In the LPC10 vocoder

The pitch and voicing are coded on 7 bits

The log of energy on 5 bits

The 10 prediction coefficients ai (transformed inki and LARi) are coded on 41 bits.

A total of 53 bits per frame of 22,5ms = 2400bps

Vector Quantization

7/30/2019 23Speech v104 Vg

29/70


Vector Quantization(2-dimensional example )

k1

k2

-1

-1

1

1

k1

k2

-1

-1

1

1

**

*

* *

sca lar q u a n t i za t i o n

. .....

.. ...

. .....

.. .... ....

.

...k1

k2

-1

-1

1

1..

...

. ..

. .

i n pu t v ect o r s (t r a i n i n g base)

...

.

. ..

vect o r qua n t i za t i on

t h e i n dex o f th e cod e

vec t or i s t r an sm i t t ed

Code

Bit rate can be decreased by applying VQ to the coefficients.

Li S F i LSF LSP

7/30/2019 23Speech v104 Vg

30/70


Line Spectrum Frequencies LSF, LSP

The Line Spectrum Frequencies fi and

Line spectrum pairs cos(fi) have goodproperties for quantization andinterpolation.

The LSF and LSP are derived from the

inverse filter A(z). Build F1(z) and F2(z) symetrical and

antisymmetrical polynomials by (for order10):

11 1 11

11 1 1

2

F ( ) ( ) ( ) /(1 ).

F ( ) ( ) ( ) /(1 ).

z A z z A z z

z A z z A z z

LSF d LSP

7/30/2019 23Speech v104 Vg

31/70


LSF and LSP

Roots of F1 and F2 on lie on the unit

circle and are interleaved. 5 conjugate roots exp(ji), fi= i/(2).

1 2

11,3,...,9

1 2

2

2,4,...,10

F ( ) 1 2 .

F ( ) 1 2 .

cos cos 2 . = LSP, = LSP.

ik

i

k

i i i i i

z q z z

z q z z

q f q f

Coders using Short Term and Long Term

7/30/2019 23Speech v104 Vg

32/70


Coders using Short Term and Long TermPrediction RELP MPE LPCELP

x(n) e(n) r(n)

Calculation

ofri ,jand ai

Calculation

ofband M

Coding of

r nu nB(z)A(z)Speechsignal

residualsignal

Analysis

Quantizationof coefficients

b, M, ai

Quantizedcoefficients

Quantized

residual

u(n)

1/A(z)1/B(z)

Synthetic

speech

signal

Synthesis

Quantized

coefficients

Quantized

residual

RPE LTP GSM F ll R t C d

7/30/2019 23Speech v104 Vg

33/70


RPE-LTP GSM Full Rate Coders

GSM Full Rate Coder is called:

RPE LTP= Regular Pulse Excited, LongTerm Prediction coder

The signal u = the best down-sampledversion ( 4) of the residual signal r.

In CELP coders, vector quantization isapplied on the signal.

CELP = Code Excited Linear Prediction

coder Each frame of residual signal is compared

to sequences of signal stored in a codebook.The codebook sequences are white and the

codebook is called stochastic codebook.

CELP C d B i S h

7/30/2019 23Speech v104 Vg

34/70


CELP Coder Basic Scheme

Analysis by synthesis (closed loop) to

find the best excitation sequence.

NormalizedStochasticCodebook

1/A(z)1/B(z)

g-

LScriterion

q

Original

speech signal x

Synthetic

Speech signal

7/30/2019 23Speech v104 Vg

35/70


Structure of CELP Coder: Perceptual Filter

Perceptual filter: the reconstruction erroris spectrally weighted exploiting noisemasking properties of formants.

W(z)=A(z/1)/A(z/ 2), 0 1, 2 1 A*(z)=A(z/) (poles towards zero)

NormalizedStochasticCodebook

1/A(z)1/B(z)

gain-

LScriterion

Error q

Original

speech signal x

Synthetic

Speech signal

W(z)

CELP C d ith P t l Filt

7/30/2019 23Speech v104 Vg

36/70


CELP Coder with Perceptual Filter

Original Signal

perceptualCriterion

1/A(z)

LPC Analysis

Syntheticspeech

Iteration on the whole codebook

Gain g

Waveformscodebook

01

2

M-

1/B(z)

Pitch estimationana-synt

Search for the bestcode and gain

W(z) MC

s

s

0 50010001500200025003000350001234567

8910

W(f)

1/A(f)

Basic CELP Structure: Perceptual Filter

7/30/2019 23Speech v104 Vg

37/70


Basic CELP Structure: Perceptual FilterInserted in the 2 Branches

Least

Squarecj

r

Speech frame

H(z)including

W(z)

W(z)

e

+-

s Weighted speech frame

Codebook

1/B(z)

e

p

H(z)=W(z)/A(z)

CELP St t M f H( )

7/30/2019 23Speech v104 Vg

38/70


CELP Structure: Memory of H(z)

Memory of H(z) = Output for a zero input

hi= impulse response of H(z)

0

0

0 1 0known

0

0

( )

( ) ( ).

( ) ( ) ( )

( ) ( ) ( ).

i n i

in n

i n i i n i i n i

i i n i

p n h e

p n h e h e h e p n

p n p n p np n s n p n

CELP Coder: Memory of H(z)

7/30/2019 23Speech v104 Vg

39/70


CELP Coder: Memory of H(z)

LeastSquare

+ -

cj

r

Speech frame

H(z)including

W(z)

W(z)

pe

+-

p0H(z) Memory

s

1/B(z)

s

Initial conditions

equal to zero

CELP: Adaptive Codebook

7/30/2019 23Speech v104 Vg

40/70


CELP: Adaptive Codebook

LTP can be realized by an adaptive codebook

0 ( ) 2 1

0 0

( ) .n n

jn in n i i n i n i M n n

i i

p p he h gc be p p

1

0

2 ( )

0

.

.

n

n i n i M

i

nj

n i n i

i

p b h e

p g h c

CELP with Stochastic Codebook

7/30/2019 23Speech v104 Vg

41/70


CELP with Stochastic Codebook

Least

Square

+ -

g1

g2c1,i(0)

c2,i(1)

Speech frame

pH(z)

W(z)

p e+-

p0H filter memory

s

H(z)

1

2

Adaptive

codebook

Stochastic

codebook

The adaptive codebook stores the past residual frames. It is calledadaptive because its content changes with time.

CELP Decoder

7/30/2019 23Speech v104 Vg

42/70


CELP Decoder

g1

g2

c1,i(0)

c2,i(1)

Synthetic speech1/A(z)

Adaptive

codebook

Stochastic

codebook

Decode received parameters:

Index of stochastic codebook

Gain of stochastic codebookIndex of adaptive codebook

Gain of adaptive codebook

Linear prediction filter coefficients

CELP E ti

7/30/2019 23Speech v104 Vg

43/70


CELP EquationsExample: Searching through Codebooks

The main load is the filtering of all thecodebook vectors.

c,i(j)H(z)

Nul Initial

conditions

f,i(j)

jth Filtered

codebook

jth Original

codebook

, ( ) , ( ) , ( )

, ( ) , ( ) , ( ) , ( )

0

. ( ) ( )* ( ).

( ) ( ) ( ). .

j j i j j i j j i j

n

j i j j i j j i j j i j

k

g f n h n c n

f n h k c n k

jp f

f Hc

Filtering Matrix H

7/30/2019 23Speech v104 Vg

44/70


Filtering Matrix H

0

1 0

2 1 0

1 2 1 0

0 0

0

0

0

N

h

h h

H h h h

h h h h

H(n) is the impulse responsecorresponding to H(z).

N = length of the codebook vectors.

Finding the Best Excitation in the Coder:

7/30/2019 23Speech v104 Vg

45/70


Finding the Best Excitation in the Coder:Equation of the Solution

J least square criterion For a set of 2 vectors cj,i (j ), F is the 2

column matrix of filtered vectors fj,i (j )

2 2

2 1

Optimal gain:

min max ( )

T T

T T T

J p p p Fg

F F g F p

J p p F F F F p

CELP Optimal Solution

7/30/2019 23Speech v104 Vg

46/70


CELP Optimal Solution

Optimal algorithm finds the best

combination of code vectors maximizingthe norm and finds the optimal gains gj.

But the number of combinations ofcodebook vectors is very high and thecomplexity is also great. Example:

M=1024 for the stochastic codebook

and M=256 for the adaptive codebook

Leads to 262 144 solutions to test and

1280 vectors to filter.

Iterative Suboptimal Algorithm for 2

7/30/2019 23Speech v104 Vg

47/70


Iterative Suboptimal Algorithm for 2Codebooks

First step:

Target vector = p

Find the best vector in the adaptivecodebook and its gain.

Calculate the new target vector p1:1

1

n np p p Second step:

Target vector = p1

Find the best vector in the stochasticcodebook and its gain.

Iterative Algorithm

7/30/2019 23Speech v104 Vg

48/70


Iterative Algorithm

-

H(z)

W(z)

p 2g 2

^

^

p 0

Speech

c(n,j)

,g 1

H(z) p 1

g 1 ^

e(n-M)

LS

M,g 2

g 1

g 2

p

p 1

LS

Operations of the Iterative Algorithm

7/30/2019 23Speech v104 Vg

49/70


Operations of the Iterative Algorithm

At step j, the optimal codebook vector

has index i:2 2

,argmin =j j j j j k

ki g p p p f

, ,

opt,

, , , ,

.

T T

j j k j j k

k T T

j k j k j k j k

g T

p f p Hc

f f c H Hc

22

opt,

, ,

arg max with

T

j j k kkT Tk

j k j k k k

i g

p Hc

c H Hc

Iterative Algorithm

7/30/2019 23Speech v104 Vg

50/70


Iterative Algorithm

Numerical Example :

FS=8000Hz,

M=256 size of the stochastic codebook

Ma=128 size of the adaptive codebook

Frame size NT=160, 20ms

Frames split in 4 subframes of N=40 samples

p=10 linear prediction order

10 Mips to filter the stochastic codebook.

Iterative Algorithm

7/30/2019 23Speech v104 Vg

51/70


Iterative Algorithm

The main processing load is the filtering

of the codebooks vectors. Many algorithms have been proposed to

decrease the computation load:

Special structures of the codebook: VSELP: Vector Sum

Algebraic codebook: ACELP

Linear codebook (the adaptive codebook is

linear). Structure of H avoiding the filtering:

Diagonalization of HTH

CELP Coding

7/30/2019 23Speech v104 Vg

52/70


CELP Coding

Standards from 4.8 kbps to 16 kbps

Federal standard (DOD) (4.8 kbps) frame = 260 samples (30 ms)

LPC 8 --> (LSP coding 34 bits)

adaptive codebook (256 vectors (fractionalpitch))

stochastic codebook (512 vectors (-1,0,1))

VSELP (Vector Sum Excitation Coding)

7/30/2019 23Speech v104 Vg

53/70


VSELP (Vector Sum Excitation Coding)

Codebook vectors v are combinations of

basis vectors (b1,b2,...,bk) v=+/- b1 +/- b2 +/- ... +/- bk

Only the basis vectors are filtered

Motorola ( 8 kbps)

GSM (half rate)(5.6 kbps)

Fractional Pitch

7/30/2019 23Speech v104 Vg

54/70


Fractional Pitch

The precision of the pitch period is a fraction ofsample TS. An interpolation filter is used.

B(z)=1-bz-Mf with Mf=M+ x(n-M-) can be written as:

TF-1(X(f)*e(-j2f(M+)Te)) = x(n-M)* TF-1(e(-j2fTe)) =x(n-M)* h(n)

h(n)s inc((n-))

+Fe/2-Fe /2f

| H(f)|

Interpolation filter H

0 2 4 6 8 10 12 14-0.5

0

0.5

1

=1/4 TS

Time in TS

Standards

7/30/2019 23Speech v104 Vg

55/70


Standards

Standards

7/30/2019 23Speech v104 Vg

56/70


Standards

Wired Telephony UIT-T

G 711 (1972) : PCM 64 kbps G 721 (1984) : ADPCM 32 kbps

G 728 (1991) : LD_CELP 16 kbps

G 729 : CS-ACELP 8 kbps

Mobile Communications (ETSI - CTIA) GSM (FR ) :RPE_LTP 13 kbps

GSM (HR) :VSELP 5.6 kbps

GSM (EFR) :ACELP 12.2 kbps

UMTS (AMR) :ACELP 12.2 to 4.75 Kbps Military applications (NATO)

FS 1015 (1976) : LPC10 2.4 kbps

FS 1016 (1991) : CELP 4.8 kbps

AMR Adaptive MultiRate Coder

7/30/2019 23Speech v104 Vg

57/70


pfor 3G Application

8 Narrow Band NB AMR source coders 12.2 10.2 7.95 7.40 6.70 5.90 5.15 4.75 kbps

9 Wide Band coders WB AMR coders

Based on ACELP

Frame of 20 ms, fs=8000 Hz

IUT Civil Standards

7/30/2019 23Speech v104 Vg

58/70


IUT Civil Standards

UITStandard

Method Year Bit rate inKbps

Delay in ms QualityMOS

Complexityin Mips

G711 PCM 1972 64 0.125 4.3

7/30/2019 23Speech v104 Vg

59/70


ETSI and Inmarsat Standards

Inmarsat

StandardMethod Year

Bit rate in

KbpsDelay in ms

Quality

MOS

Complexity

in Mips

IMBE IMBE 1990 4.15 78.75 3.4 7

Mini-M AMBE 1995 3.6 3.6 25

Standard

ETSIeurope

Method Year

Bit rate in

Kbps Delay in ms

Quality

MOS

Complexity

in Mips

GSM RPE-LTP 1987 13 40 3.47 6

TETRA ACELP 1994 4.567 67.5 12

GSM HR VSELP 1994 5.6 45 30

EFR-GSM

(identicalDCS)

ACELP 1995 12.8 40 15

TIA and RCR Standards

7/30/2019 23Speech v104 Vg

60/70


TIA and RCR Standards

TIA

standard

USA

Method Year Bit rate in

KbpsDelay in ms

Quality

MOS

Complexity

in Mips

IS54 VSELP 1989 7.95 45 3.5 20

IS96 QCELP 1993 8.5-4.2-0.8 45 4 20

IS136 ACELP 1995 7.4 45

IS127 EVRC RCELP 1995 8.5-4-0.8 >45 20

PCS 1900 ACELP 1995 40 15

TIA

standard

USA

Method Year Bit rate in

KbpsDelay in ms

Quality

MOS

Complexity

in Mips

RCR STD-

27BVSELP 1990 6.7 45 20

JDC HR PSI-CELP 1993 3.45 90 50

Implementation of CELP Coders on C54x

7/30/2019 23Speech v104 Vg

61/70


Implementation of CELP Coders on C54x

Example of the G729 Annex A.

Specific instruction for codebook search

Some functions of DSPLIB

Profiling Example for G729 Annex A

7/30/2019 23Speech v104 Vg

62/70


g pusing C Compiler

G729 is a CS-ACELP Coder (ITU 1995) 8Kbps with quality of ADPCM at

32Kbps G726.

DSVD: G729 Annex A voice overinternet, voice e-mail

Digital Simultaneous Voice & Data

G729 Annex A

7/30/2019 23Speech v104 Vg

63/70


Main Blocks of the Coder Algorithm

Frame = 10 ms = 80 Samples.

Short term LPC analysis on 40ms frame

LSP derived from ai coefficients andquantized using Split VQ.

Long Term LTP analysis, 2 subframesof 40 samples.

LTP lag and gain. LTP fractional lag (1/3)

8 bits 1rst

subframe and 5 bits for the 2nd

. Search fixed codebook: 2 subframes of

40 samples. Index and gains

Code length = 40 with 4 non-zero pulses 1.

Structures of Frames

7/30/2019 23Speech v104 Vg

64/70


Previous speech

120

Subframe 1

40

Subframe 2

40

Next frame

40

Present frame, 80 samples = L_Frame

New speech, 80 samples = L_Frame

Total speech vector, 240 samples = L_Total

LPC analysis window, 240 samples = L_Window

G729 Annex A, Bit Allocation

7/30/2019 23Speech v104 Vg

65/70


,

Total inbits

18

18

13

1

26

8

1480

1rst subframe(5ms) in bits

2nd subframe(5ms) in bits

LPC filter

pitch period

parity check

Fixed codebook

Pulse positions

Pulse signs

Codebook gains

8

1

7 = 3+4

4

Adaptive codebook:

7 = 3+4

13 = (3x3+4)

5

13 = (3x3+4)

4

G729 Annex A

7/30/2019 23Speech v104 Vg

66/70


Main Blocks of the Decoder Algorithm

The serial received bits are converted intoparameters:

LSP vector, 2 fractional pitch lags and gains, 2fixed codebook index and gains.

LSP are converted to LP filter coefficientsai and interpolated at each subframe.

At each subframe:

The excitation is constructed and scaled.

The speech is synthesized by filtering theexcitation by the LP synthesis filter.

Postprocessing by an adaptive postfilter.

Using the C Compiler

7/30/2019 23Speech v104 Vg

67/70


g p

Use the C program of the standard and Ccompiler with maximum optimization.

Autocorrelation = 488 445 cycles

Levinson = 164 843 cycles

Conversion ai LSF = 410 404 cycles

LSF Quantization = 883 853 cycles

Synthesis filtering = 501 472 cycles

Pitch open loop = 793 533 cycles

Fractional Pitch = 2 x 618 354 cycles Search Algebraic code = 2x 617 582 cycles

Gains quantization = 2x 108 480 cycles

Assembly Language Instructions for

7/30/2019 23Speech v104 Vg

68/70


Codebook Search Better results can be obtained with

assembly language than C. Specific instructions for codebook

search: Conditional stores.

CodeBook Search (Conditional Stores)STRCD Xmem, cond

SRCCD Xmem, cond

SACCD src, Xmem, cond

Xmem = T if condition is true

Xmem = BRC if condition is true

Xmem = src if condition is true

STRCD = Store T Conditionally

SRCCD = Store Block Repeat Counter Conditionally

SACCD = Store Accumulator Conditionally

Assembly Language Codebook Search

7/30/2019 23Speech v104 Vg

69/70


y g g

2 2

i

Best code book index

carg max noted find max of

G

opt

iopt

ii i

j

j

222 2

To avoid division:

.opti

i opt opt i

i opt

ccc G c G

G G

Assembly Language Codebook Search

7/30/2019 23Speech v104 Vg

70/70

y g g

.mmregs

.text

CBS: STM #C, AR5

STM #G, AR2STM #G-opt, AR3

STM #I-opt, AR4

ST #0, *AR4

ST #1, *AR3+

ST #0, *AR3-STM #N-1, BRC

RPTBD done

SQUR *AR5+, A

MPYA *AR3+

MAS *AR2+, *AR3-, B

SRCCD *AR4, BGEQSTRCD *AR3+, BGEQ

SACCD A, *AR3-, BGEQ

SQUR *AR5+, Adone: MPYA *AR3+

R5 C(0)

...

R3 Gopt=1

Copt2=0

R4 Iopt=0

R2 G(0)

...

A=C(i)2

B=C(i)2Gopt T=Gopt

B=C(i)2Gopt-G(i)Copt2

If (B 0) then: BRC Gopt T Iopt A C t2

Date post:	14-Apr-2018
Category:	Documents
Upload:	senthil-kumar
View:	230 times
Download:	0 times

23Speech v104 Vg

Documents