SPEECH CODING

Maryam ZebarjadAlessandro Chiumento

Supervisor : Sylwester Szczpaniak

Outline

Properties of speech signals Why coding ? Implemented tecniques

Differential Pulse-Code Modulation DCT Tranfrorm Coder LPC Vocoder

Results

SPEECH PROPERTIES

Speech is produced when air is forced from the lungs through the vocal cords and along the vocal tract.

It can be modeled by two states:

Voiced Speech: - produced by the vibrations of the vocal cords.- quasi-periodic in the time domain and harmonically

structured in the frequency domain.

Unvoiced Speech: - produced , for example, by high speed air passing through a constriction in the vocal tract (mouth and

lips)- random-like and broadband (like white noise).

Why coding ?

Original speech signal has to be processed in order to be :

MINIMIZE DIMENSIONS (storage)

MINIMIZE BITRATE (transmission)

VOIPMOBILE TELEPHONY

DPCM

We have done DPCM about a wave file and here is the result for different prediction orders:

-we have the coder and decoder signal for the prediction orders of 1, 2, 5, 10, 19.

-we have corresponding wave files for each stage-we also have the SNR for each prediction order

For the auto correlation method these were the basic formula as previously stated

The DPCM Method with autocorrelation

The Sriginal Signal

Coder Signal for Prediction Order of 1

Decoder Signal for Prediction Order of 1

Coder Signal for the Prediction order of 2

Decoder Signal for the Prediction Order of 2

Decoder Signal for the Prediction Order of 2

Coder Signal for the Prediction Order of 5

Decoder Signal for the Prediction Order of 5

Coder Signal for the Prediction Order of 10

Decoder Signal for the Prediction Order of 10

Coder Signal for the Prediction Order of 19

Decoder Signal for the Prediction Order of 19

SNR

Then by the following formula we calculate the Decoder SNR for each prediction order

LPC VocoderVocoders rely strongly on the properties of speech.

Two – state excitation model: - pulses for voiced signal- random noise for unvoiced

signalVocal tract is modeled as an all-pole function.

Source-System synthesis model

where

LPC Vocoder

We have to find: - pitch period- gain- poles of the system

LPC Vocoder

V/UV DETECTION is done by taking the energy of each frame and compare it to a threshold. Taking the zero-crossing rate and compare it to a threshold.

PITCH DETECTION is done by Autocorrelation method : we cross-correlate the signal with it self,

the output has a max after the pitch period.

POLES OF THE SYSTEM are estimated using: LPC, in our case the LEVINSON-DURBIN algorithm

GAIN IS ESTIMATED : If the frame is UnVoiced we take the sqrt of the average power of

the frame. If the frame is Voiced we use the average power for every pitch

period.

LPC VocoderORIGINAL SAMPLE

SYNTHETIZED SAMPLES

DCT Transform Coder

There is no standard Same structure than vocoder

DCT Transform Coder

Discrete Cosine Trasform is a unitary transform that expresses the incoming signal as a finite sum of cosine functions:

So if the signal is periodic we need a “small” number of cosines (coefficients)insteadif the signal is non periodic the cosines have to be many more.

DCT Transform CoderVoiced frame : waveform DCT coefficients

Unvoiced frame : waveform DCT coefficients

DCT Transform CoderORIGINAL SAMPLE

Synthetized sample 22.5ms720 coeff V1460 coeff UV

22.5ms40 coeff V1460 coeff UV

50ms720 coeff V1460 coeff UV