Speech & Audio Processing

1. Speech & Audio Processing Speech & Audio Coding Examples

2. A Simple Speech Coder • LPC Based Analysis Structure Linear Prediction Analysis Levinson-Durbin Pre-emphasis WindowingAnalysis Auto-Correlation AudioInput Residual Residual AnalysisFilter Quantization Filter Coeffs Filter Coeffs Veton Këpuska

3. Windowing Analysis Stage N – Length of the Analysis Window 10-30 msec Veton Këpuska

4. Some Analysis Windows Veton Këpuska

5. MATLAB Useful Functions • wintool • Use “doc wintool” for more information • window • Use “>doc window” for the list of supported windows • Define your own window if needed e.g: • Sine window and Vorbis window Veton Këpuska

6. LPC Analysis Stage • LPC Method Described in: • Ch5-Analysis_&_Synthesis_of_Pole-Zero_Speech_Models.pptx • Summary: • Perform Autocorrelation • Solve system of equations with Durbin-Levinson Method • MATLAB help • doc lpc, etc. Veton Këpuska

7. Example of MATLAB Code function myLPCCodec(wavfile, N) % % wavfile - input MS wav file % N - LPC Filter Order % Vocal TractModel VoicedUnvoiced ImpulseTrain ŝ[n] NoiseGenerator Gain Veton Këpuska

8. Analysis of Quantization Errors • Use MATLAB functions to research the effects of quantization errors introduced by precision of the arithmetic operations and representation of the filter and error signal: • Double (float64) representation (software emulation) • Float (float32) representation (software emulation) • Int (int32) representation (hardware emulation) • Short (int16) representation (hardware emulation). • Useful MATLAB functions: • Fix, floor, round, ceil • Example: • sig_hat=fix(sig*2^(B-1))/2^(B-1); • Truncation of the sig to B bits. Veton Këpuska

9. Quantization of Error Signal & Filter Coefficients • Can Apply ADPCM for Error Signal • Filter Coefficients in the Direct Filter Form are found to be sensitive to quantization errors: • Small quantization error can have a large effect on filter characteristics. • Issue is that polynomial coefficients have non-linear mapping to poles of the filter (e.g., roots of the polynomial). • Alternate representations possible that have significantly better tolerance to quantization error. Veton Këpuska

10. LPC Filter Representations • As noted previously when Levinson-Durbin algorithm was introduced one alternate representation to filter coefficients was also mentioned: PARCOR coefficients: • LPC to PARCOR: Veton Këpuska

11. PARCOR Filter Representation • PARCOR to LPC: Veton Këpuska

12. Line Spectral Frequency Representation • It turns out that PARCOR coefficients can be represented with LSF that have significantly better properties. • Note that: • The PARCOR lattice structure of the LPC synthesis filter above: Input Output Ap-1 A0 Ap + + kp kp-1 k0=-1 kp+1=∓1 - - z-1 z-1 z-1 Bp-1 B0 Bp Veton Këpuska

13. Line Spectral Frequency Representation • From previous slide the following holds: • From this realization of the filter the LSP representation is derived: Veton Këpuska

14. LSF Representation Veton Këpuska

15. LPC Synthesis Filter with LSF Veton Këpuska

16. A Simple Speech Coder • LPC Based Synthesis Structure ResidualSignal De-emphasis SynthesisFilter AudioOutput Residual Decoding Filter Coeffs FilterCoeffs Veton Këpuska

17. Audio Coding

18. Audio Coding • Most of the Audio Coding Standards use principles of Psychoacoustics. • Example of Basic Structure of MP3 encoder: AudioInput Bit-stream Filterbank &Transform Quantization PsychoacousticModel Veton Këpuska

19. Basic Structure of Audio Coders • Filterbank Processing • Psychoacoustic Model • Quantization Veton Këpuska

20. Filter Bank Analysis Synthesis

21. Filterbank Processing: • Splitting full-band signal into several sub-bands: • Uniform sub-bands (FFT) • Critical Band (FFT followed by non-linear transformation) • Reflect Human Auditory Apparatus. • Mel-Scale and Bark-Scale transformations Veton Këpuska

22. Mel-Scale Veton Këpuska

23. Bark-Scale Veton Këpuska

24. Analysis Structure of Filterbank hk[n] – Impulse Response of a Quadrature Mirror kth-filter N – Number of Channels. Typically 32 ↓ - Down-sampling MDCT – Modified Discrete Cosine Transform h1[n] ↓ MDCT MDCT Bit Stream AudioInput hk[n] ↓ MDCT MDCT Quantization hN[n] ↓ MDCT MDCT Veton Këpuska

25. Analysis Structure of Filterbank gk[n] – Impulse Response of a Inverse Quadrature Mirror kth-filter N – Number of Channels. Typically 32 ↑ - Up-sampling IMDCT – Inverse Modified Discrete Cosine Transform IMDCT ↑ g1[n] MDCT Bit Stream AudioOutput IMDCT ↑ gk[n] MDCT Decoding IMDCT ↑ gN[n] MDCT Veton Këpuska

26. Psycho-Acoustic Modeling

27. Psychoacoustic Model • Masking Threshold according to the human auditory perception. • Masking threshold is used to quantize the Discrete Cosine Transform Coefficients • Analysis is done in frequency domain represented by DFT and computed by FFT. Veton Këpuska

28. Threshold of Hearing • Absolute threshold of audibly perceptible events in quiet conditions (no other sounds). • Any signal bellow the threshold can be removed without effect on the perception. Veton Këpuska

29. Threshold of Hearing Veton Këpuska

30. Frequency Masking • Schröder Spreading Function • Bark Scale Function: Veton Këpuska

31. Masking Curve Veton Këpuska

32. Primary Tone 1kHz Veton Këpuska

33. Masked Tone 900 Hz Veton Këpuska

34. Combined Sound 1kHz + 0.9kHz Veton Këpuska

35. Combined 1kHz + 0.9kHz (-10dB) Veton Këpuska

36. Combined 1kHz + 5kHz (-10dB) Veton Këpuska

37. END Veton Këpuska