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Speech and Audio Coding

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Speech and Audio Coding. Heejune AHN Embedded Communications Laboratory Seoul National Univ. of Technology Fall 2013 Last updated 2013. 9. 31. Audio Coding. Audio signal classes speech signal : 300Hz - 3400Hz (< 4KHz) For telephone service, e.g. VoIP, mobile voice telephony

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Speech and Audio Coding

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  1. Speech and Audio Coding Heejune AHN Embedded Communications Laboratory Seoul National Univ. of Technology Fall 2013 Last updated 2013. 9. 31

  2. Audio Coding • Audio signal classes • speech signal : 300Hz - 3400Hz (< 4KHz) • For telephone service, e.g. VoIP, mobile voice telephony • wide speech signal : 50 - 7000Hz • High quality voice telephony. e.g . Skype, W-AMR in 3.5/4 G • wideband audio signal : - 20KHz • Entertainment. E.g. CD, mp3, DVD, broadcasting, cinema movies • CD example • Sampling frequency : 44.1 KHz • 16-bits/ sample • two stereo channels • net bit-rate = 2 x 16 x 44.1 x 10 = 1.41 Mbits/sec. • real bit-rate = 1.41 x 49/16 Mbit/sec = 4.32 Mbit/sec. • for synchronization and error correction, 49 bits for every 16-bit audio sample.

  3. Audio coding techniques • Speech coding • Sound generation model is well studied • Wave form coding (time-domain) • Linear PCM, Non-linear PCM, DPCM, ADPCM • Vocoder • Use speech specific speech production model • Analysis at encoders and Synthesis at decoders. • LP-Vocoder, RPE (regular pulse excited) Vocoder (GSM), Q-CELP (qualcomm code-excited Linear prediction) (CDMA), AMR (Adaptive Multi-Rate) (Algebric code excited LP, 3G, 4G) • Audio coding • No sound generation model yet. • Human auditory system and psychoacoustic perception • MPEG1L1, 2, 3, MPEG-2 AAC, Dolby AC-3

  4. Speech Coding • Signals

  5. Linear PCM • Linear • Uniform Quantizer, i.e., quantization levels are evenly spaced. • SNR = 6 * no of bits + C db • at least 6*12 db required for human intelligible • 16-bit samples provide plenty of dynamic range. • Application • In CD, File formats of WAV (MS), AIFF (Unix and Mac)

  6. Nonlinear PCM • Nonlinear • Non-uniform quantization • Quantization step-size decreases logarithmically with signal level • Using that the human audio perception is logarithm-scale • Companding • Compress and Expand before Uniform quantization • u-law and A-law

  7. Mu law • Provides 14-bit quality (dynamic range) with an 8-bit encoding • Compression factor 2:1. • au (Sun audio file format). • in North American & Japanese ISDN voice service

  8. DPCM • Predictive Coding • Transmit the difference (rather than the sample). • Difference between 2 x-bit samples can be represented with significantly fewer than x-bits

  9. Slope-overload problem in DPCM • Prediction differences x(n) are too large for the quantizer to handle. Encoder fails to track rapidly changing signals. • Especially near the Nyquist frequency.

  10. ADPCM • Adaptive step size • a larger step-size for fast-varying (high-frequency) samples; a smaller step-size for slowly varying samples. • Step size parameter is not transmitted; Use previous sample values to estimate changes in the signal in the near future.

  11. Speech Production Model • Lung • Glottis/vocal cord : 성대 (pulse) • Vocal track (filter) • Cavities (articulation) • Organ model: change the vocal track filter • 2 Modes • voiced sound: vocal cord is vibrating (quasi-periodic excitation) • Unvoiced sound : vocal cord is open (noise like model) glittis cavities Lung Vocal cord Vocal track Lips/tungs

  12. Signal and System modeling • Voiced Speech Lambda = 4L c= 340m/s Formant Frequencies f1= 340/4*0.17 = 500 Hz f3 = 1500 Hz f5 = 2500 Hz Time varying Linear system Excitation generation Vocal track + Radiation effect Train of glottal pulse T = pitch frequency T

  13. Unvoiced Speech • No tonic excitation, no formant frquencies

  14. Linear Predictive Coding • LPC • A generalization of ADPCM • Linear prediction model • Prediction parameters : changes slowly relatively the data rate … synthesis decoder M/sec Analysis Encoder at N /sec at N /sec at N/sec

  15. Speech analysis and synthesis • In every 5 to 40 m • Analysis • Extract pitch frequencies • Spectral analysis : Using multiple band-pass filers. • Synthesis • Based on mode, excitation signal generate into the vocal track filter.

  16. LPC analyzer decoder coder S(n) Pitch Detector (period, gain) Voice/unvoiced ? LPC synthesizer

  17. LPC Vocoder

  18. Bandwidth efficiency

  19. Psychoacoustic Principles • Psycho-acoustic • Human auditory perception property, similar to HVS in Video Coding • Average human does not hear all frequencies the same way. • Limitations of the human sensory system leads to facts that can be used to cut out unnecessary data in an audio signal. • Key Properties • Critical Band Property • Masking Property ‘ • Absolute Threshold of hearing • Auditory masking

  20. CriticalBands • Human auditory • Limited and frequency dependant Resolution • 25 critical bands • Bands • 1 Bark (e.g. Band) = the width of one critical band • Critical band number (Bark) for a given frequency, z(f): • f < 500Hz => z(f) ≈ f/100 • f > 500Hz => z(f) ≈ 9 + 4 log2(f/1000)

  21. Absolute threshold of hearing • Range: 20 Hz - 20 kHz, most sensitive at 2 kHz to 4 kHz. • Dynamic range (quietest to loudest is about 96 dB) • Normal voice range is about 500 Hz - 2 kHz.

  22. Simultaneous Masking • Masking Effects • The presence of tones at certain frequencies makes us unable to perceive tones at other “nearby” frequencies • Humans cannot distinguish between tones within 100 Hz at low frequencies and 4 kHz at high frequencies.

  23. Approximates a triangular function modeled by spreading function, SF (x) (db), where x has units of bark less sleep sleeper

  24. Example • Hopefully we can built demo.

  25. TemporalMasking • If we hear a loud sound, then it stops, it takes a little while until we can hear a soft tone nearby. • As much as 50 ms before and 200 ms after. • Example; • Play 1 kHz masking tone at 60 dB and 1.1 kHz test tone at 40dB.

  26. MPEG-1Audio • MP3 • Mostly popular audio format at present. • denotes MPEG-1 Audio Level 3, not MPEG-3 (no such a thing in the world) • Part of MPEG-1 Standards • 1.2 Mbps for video + 0.3 Mbps for audio • Sampling frequency • 32, 44.1 and 48 kHz • One or two audio channels • Monophonic, Dual-monophonic, Stereo, Joint Stereo • Compression ratio from 2.7:1 to 42:1 • Uncompressed CD audio - 44,100 samples/sec * 16 bits/sample * 2 ch > 1.4 Mbps • 16 bit stereo sampled at 48 kHz is reduced to 256 kbps

  27. MPEG-1AudioLevels • Level of Complexity • Layer 1 • DCT type filter with one frame and equal frequency spread per band. • Psychoacoustic model only uses frequency masking. • Layer 2 • Use three frames in filter (before, current, next, a total of 1152 samples). • This models a little bit of the temporal masking. • Layer 3 (Known as MP3) • Better critical band filter is used (non-equal frequencies) • Psychoacoustic model includes temporal masking effects, and takes into account stereo redundancy. • Huffman coder.

  28. BlockDiagram for Level 1

  29. Sub-band Filter Part

  30. Subband Bank • 32 PCM samples yields 32 subband samples. • Each sub-band evenly spaced, not like critical bands’ width • For example, @48 kHz sampling rate, each sub-band is 750 Hz wide. • Frame • Samples out of each filter are grouped into blocks, called frames. • Blocks of 12 for Layer 1 (384 samples). • Blocks of 36 for Layers 2 and 3(1152 samples) @48 kHz, 32x12 represents 8ms of audio.

  31. Psychoacousticanalysis • FFT gets detailed spectral information about the signal. • 512-point FFT for Layer 1, 1024-point for Layer 2 and 3 • Don’t be confused the subband filter input samples! • Tonal and NonTonal masker from each band • Determine the minimal masking threshold in each subband. (using global threshold and masking threshold) • Calculate the signal-to-mask ratio (SMR) in each subband, • This can be considered a quantization margin.

  32. Segment the signal into 512 samples => 12 ms frames • @44.1kHz

  33. Maskingexample • Suppose the levels of the first 16 of the 32 sub-bands are: • Band !1 !2 !3 !4 !5 !6 !7 !8 !9 !10 !11 !12 !13 !14 !15 !16! • Level !0 !8 !12 !10 !6 !2 !10 !60 !35 !20 !15 !2 !3 !5 !3 !1! (dB) • The level of the 8th band is 60 dB, the pre-computed masking model specifies a masking of 12 dB in the 7th band and 15 dB in the 9th. • The signal level in 7th band is 10 (< 12 dB), so ignore it. • The signal level in 9th band is 35 (< 15 dB), so send it. • Only the signals above the masking level needs to be sent.

  34. Scaling • In order to use full range of quantizer • Encoder : divide signal by scale factor before quantization • Decoder : multiply by scale factor after quantization • Scale Factor • Largest signal quantized using 6-bit scale-factor. • The receiver needs to know the scale factor and quantisation levels used. • Information included along with the samples • The resulting overhead is very small compared with the compression gains.

  35. Bit allocation and Quantization • Constraints • the target bit rate. 192 kbps target rate => 8 ms/frame @48KHz => ~1.5 kbits/frame. • Objective • Objective is to minimize noise-to-mask ratio (NMR) over all sub-bands, i.e., minimize quantization noise. • Mission • For each audio frame, bits must be distributed across the sub-bands from a predetermined number of bits defined by bit-allocation frames 12 data/ frame/band 1/scaling factor quantizer

  36. A solution for bit allocation • More bits for low Mask Level (since noise is easily noticed) • Iterative process • For each sub-band, determine NMR = SNR – SMR • Allocate bits one at a time to the sub-band with the largest NMR. Recalculate NMR values.Iterate until all bits used.

  37. Output Frame • Frame format • Framelen - frame length in bytes; • Bit-allocation: • 4 bits allocated to each subband (0-31). • 2-15 bits, ‘0000’ indicates no sample. • Scale-factors - 6 bits • Multiplier that sizes the samples to fully use the range of the quantizer • Has variable length depending on N = the number of subbands with non-zero bit allocation. • Audio Data • subbands 0-31 stored in order, with each subband including 12 samples

  38. Level 3 • Block Diagram

  39. Filter-bank Improved (MDCT) • Equally spaced Sub-bands do not accurately reflect the ear’s critical bands. • f < 500Hz => z(f) ≈ f/100 • f > 500Hz => z(f) ≈ 9 + 4 log2 (f/1000) • Each sub-band further analyzed using Modified DCT(MDCT) • create 18 samples (for total of 576 samples) . • Better tracking of masking threshold. • MP3 also specifies a MDCT block length of 6. • Lots of bit allocation options for quantizing frequency coefficients. • Huffman code • For Quantized coefficients

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