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Speech Coding (Part I)  Waveform Coding

Speech Coding (Part I)  Waveform Coding. 虞台文. Content. Overview Linear PCM (Pulse-Code Modulation) Nonlinear PCM Max-Lloyd Algorithm Differential PCM (DPCM) Adaptive PCM (ADPCM) Delta Modulation (DM). Speech Coding (Part I)  Waveform Coding. Overview.

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Speech Coding (Part I)  Waveform Coding

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  1. Speech Coding (Part I)  Waveform Coding 虞台文

  2. Content • Overview • Linear PCM (Pulse-Code Modulation) • Nonlinear PCM • Max-Lloyd Algorithm • Differential PCM (DPCM) • Adaptive PCM (ADPCM) • Delta Modulation (DM)

  3. Speech Coding (Part I)  Waveform Coding Overview

  4. Classification of Coding schemes • Waveform coding • Vocoding • Hybrid coding

  5. Quality versus Bitrate of Speech Codecs

  6. Waveform coding • Encode the waveform itself in an efficient way • Signal independent • Offer good quality speech requiring a bandwidth of 16 kbps or more. • Time-domain techniques • Linear PCM (Pulse-Code Modulation) • Nonlinear PCM: -law, a-law • Differential Coding: DM, DPCM, ADPCM • Frequency-domain techniques • SBC (Sub-band Coding) , ATC (Adaptive Transform Coding) • Wavelet techniques

  7. Vocoding • ‘Voice’ + ‘coding’ . • Encoding information about how the speech signal was produced by the human vocal system. • These techniques can produce intelligible communication at very low bit rates, usually below 4.8 kbps. • However, the reproduced speech signal often sounds quite synthetic and the speaker is often notrecognisable. • LPC-10 Codec: 2400 bps American Military Standard.

  8. Hybrid coding • Combining waveform and source coding methods in order to improve the speech quality and reduce the bitrate. • Typical bandwidth requirements lie between4.8 and 16 kbps. • Technique: Analysis-by-synthesis • RELP (Residual Excited Linear Prediction) • CELP (Codebook Excited Linear Prediction) • MPLP (Multipulse Excited Linear Prediction) • RPE (Regular Pulse Excitation)

  9. Quality versus Bitrate of Speech Codecs

  10. Speech Coding (Part I)  Waveform Coding Linear PCM (Pulse-Code Modulation)

  11. Pulse-Code Modulation (PCM) • A method for quantizing an analog signal for the purpose of transmitting or storing the signal in digital form.

  12. Quantization • A method for quantizing an analog signal for the purpose of transmitting or storing the signal in digital form.

  13. Linear/Uniform Quantization

  14. Quantization Error/Noise

  15. Quantization Error/Noise overload noise overload noise granular noise

  16. Quantization Error/Noise

  17.  Quantization Step Size Quantization Error/Noise Unquantized sinewave 3-bit quantization waveform 3-bit quantization error 8-bit quantization error

  18. +   +  Quantization Step Size The Model of Quantization Noise

  19. Signal-to-Quatization-Noise Ratio (SQNR) • A measurement of the effect of quantization errors introduced by analog-to-digital conversion at the ADC.

  20. Signal-to-Quatization-Noise Ratio (SQNR) Assume

  21. Signal-to-Quatization-Noise Ratio (SQNR) Assume Is the assumption always appropriate?

  22. Signal-to-Quatization-Noise Ratio (SQNR) Each code bit contributes 6dB. constant The term Xmax/x tells how big a signal can be accurately represented

  23. Signal-to-Quatization-Noise Ratio (SQNR) Determined by A/D converter. Depending on the distribution of signal, which, in turn, depends on users and time.

  24. Signal-to-Quatization-Noise Ratio (SQNR) In what condition, the formula is reasonable?

  25. midtread midrise Overload Distortion

  26. midtread midrise Assume Probability of Distortion

  27. midtread midrise Assume Probability of Distortion

  28. midtread midrise Assume Overload and Quantization Noise withGaussian Input pdf and b=4

  29. Uniform Input Pdf Gaussian Input Pdf Uniform Quantizer Performance

  30. More on Uniform Quantization • Conceptually and implementationally simple. • Imposes norestrictions on signal's statistics • Maintains a constantmaximum error across its total dynamic range. • xvaries so much (order of 40 dB) across sounds, speakers, and input conditions. • We need a quantizing system where the SQNR is independent of the signal’s dynamic range, i.e., a near-constantSQNR across its dynamic range.

  31. Speech Coding (Part I)  Waveform Coding Nonlinear PCM

  32. Probability Density Functionsof Speech Signals Counting the number of samples in each interval provides an estimate of the pdf of the signal.

  33. Probability Density Functionsof Speech Signals

  34. Probability Density Functionsof Speech Signals • Good approx. is a gamma distribution, of the form • Simpler approx. is a Laplacian density, of the form:

  35. Probability Density Functionsof Speech Signals • Distribution normalized so that x=0 and x=1• • Gamma density more closely approximates measured distribution for speech thanLaplacian. • Laplacian is still a good model in analytical studies. • Smallamplitudes much more likely than large amplitudes—by 100:1 ratio.

  36. Companding • The dynamic range of signals is compressed before transmission and is expanded to the original value at the receiver. • Allowing signals with a large dynamic range to be transmitted over facilities that have a smaller dynamic range capability. • Companding reduces the noise and crosstalk levels at the receiver.

  37. Companding Compressor Uniform Quantizer Expander

  38. Companding Compressor Uniform Quantizer Expander

  39. Companding After compression, yis Nearly uniformly distributed Compressor Uniform Quantizer Expander

  40. The Quantization-Error Variance of Nonuniform Quantizer Compressor Uniform Quantizer Expander Jayant and Noll

  41. The Quantization-Error Variance of Nonuniform Quantizer Compressor Uniform Quantizer Expander Jayant and Noll

  42. Jayant and Noll The Optimal C(x) If the signal’s pdf is known, then the minimum SQNR, is achievable by letting Compressor Uniform Quantizer Expander

  43. Jayant and Noll The Optimal C(x) If the signal’s pdf is known, then the minimum SQNR, is achievable by letting Is the assumption realistic. Compressor Uniform Quantizer Expander

  44. PDF-Independent Nonuniform Quantization Assuming overload free, We require thatSQNRis independent onp(x).

  45. Logarithmic Companding

  46. -Law & A-Law Companding • -Law • A North American PCM standard • Used by North America and Japan • A-Law • An ITU PCM standard • Used by Europe

  47. -Law & A-Law Companding • -Law • A North American PCM standard • Used by North America and Japan • A-Law • An ITU PCM standard • Used by Europe (=255  in U.S. and Canada) (A=87.56  in Europe)

  48. -Law & A-Law Companding

  49. -Law Companding

  50. -Law Companding

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