1. SPEECH CODING Maryam Zebarjad Alessandro Chiumento Supervisor : Sylwester Szczpaniak

2. Outline Properties of speech signals Why coding ? Implemented tecniques Differential Pulse-Code Modulation DCT Tranfrorm Coder LPC Vocoder Results

3. 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).

4. Why coding ? Original speech signal has to be processed in order to be :  MINIMIZE DIMENSIONS (storage)  MINIMIZE BITRATE (transmission) VOIPMOBILE TELEPHONY

5. 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

6. The DPCM Method with autocorrelation

7. The Sriginal Signal

8. Coder Signal for Prediction Order of 1

9. Decoder Signal for Prediction Order of 1

10. Coder Signal for the Prediction order of 2

11. Decoder Signal for the Prediction Order of 2

13. Coder Signal for the Prediction Order of 5

14. Decoder Signal for the Prediction Order of 5

15. Coder Signal for the Prediction Order of 10

16. Decoder Signal for the Prediction Order of 10

17. Coder Signal for the Prediction Order of 19

18. Decoder Signal for the Prediction Order of 19

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

20. LPC Vocoder Vocoders rely strongly on the properties of speech. Two – state excitation model: - pulses for voiced signal - random noise for unvoiced signal Vocal tract is modeled as an all-pole function. Source-System synthesis model where

21. LPC Vocoder We have to find:- pitch period - gain - poles of the system

22. 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.

23. LPC Vocoder ORIGINAL SAMPLE SYNTHETIZED SAMPLES

24. DCT Transform Coder There is no standard Same structure than vocoder

25. 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) instead if the signal is non periodic the cosines have to be many more.

26. DCT Transform Coder Voiced frame : waveform DCT coefficients Unvoiced frame : waveform DCT coefficients

27. DCT Transform Coder ORIGINAL SAMPLE Synthetized sample 22.5ms 720 coeff V 1460 coeff UV 22.5ms 40 coeff V 1460 coeff UV 50ms 720 coeff V 1460 coeff UV