Lecture 16 Perceptual Audio Coding

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1 EECS 225D Audio Signal Processing in Humans and Machines Lecture 16 Perceptual Audio Coding Professor Nelson Morgan today s lecture by John Lazzaro Hero sound Play

2 Today s lecture: Audio Coding Compression: Lossless and Lossy Quantization and Noise Psychoacoustic Masking Time-Frequency Tradeoffs Research Topics

3 OS X System Sound: Hero.aiff 1 second of 44.1 khz, 16-bit, stereo audio 186, 450 byte disk file, Mb/s b = bit, B = 8 bit bytes Play

4 How well does gzip work on audio files? File size reduced by a factor of 1.56 (measured in units of 4KB disk blocks) Lossless compression. (decompression is bit-accurate). Shorten : Tony Robinson, Cambridge, Lossless algorithms remove redundant bits -- bits that are not needed to exactly reconstruct the original file. Redundancy removal can be improved if the algorithm can be specialized for audio waveforms.

5 Apple Lossless (after shorten, FLAC,...) File size reduced by a factor of 3. 1 (double the performance of gzip on the same file) Lossless, just like gzip. To reduce file size by larger factors, we need to go beyond removing redundancy. One approach: Remove information that is irrelevant information for a particular use case. Example: Remove audio information whose loss a human listener cannot perceive. Lossy perceptual audio coding.

6 MPEG 4 Advanced Audio Codec (AAC) Request 128 kb/s Encoder adjusts quality to meet request File size reduced by a factor of 9.4 Today s Lecture How it works Lossy: Decompression does not restore original file. Listening Test: Original: 128 kb/s (9.4X): 16 kb/s (23.5X): Play Play Play

7 Today s lecture: Audio Coding Compression: Lossless and Lossy Quantization and Noise Psychoacoustic Masking Time-Frequency Tradeoffs Research Topics

8 Quantization, noise, and compression... To compress a real-valued discrete-time waveform, quantize the samples to reduce bits/sample, and then apply lossless compression. s(t) Play s(t) + e(t) Play Quantization corrupts the signal s(t) with noise term e(t). In this example, quantizing to 1 bit is clearly objectionable. However, a 40 db reduction in e(t) yield a better result. Quantizing with more bits s(t) *e(t) Play acts to reduce e(t). s(t) + 0.1*e(t) Play

9 Which leads to this architecture... Input Subband 1 quantized samples Subband M quantized samples Encoder Bandpass Analysis filters Decoder Q -1 [ ] Dequantize Q -1 [ ] Downsample M f f M M Upsample M (M subbands) Quantize Q[ ] Q[ ] Reconstruction filters f f Subband 1 quantized samples Subband M quantized samples + Output Filter bank splits audio input into M sub-bands. Quantize to minimize the number of bits needed across all M channels. Constraint: Human imperceptibility of the encode -> decode process.

10 Quantization noise in a sub-band... t Encoder Bandpass Analysis The noise floor is 6*B db below the tone. f Downsample M level SNR ~ 6 B Quantize Q[ ] Decoder Q -1 [ ] Reconstruction Masking tone A tone in the sub-band, scaled to fully use B quantize bits. Masked threshol S M f Quantization noise Subband N freq (Approximate result. See the book for the fine print)

11 If a B is too small, noise may be audible Encoder Bandpass f Downsample M Quantize Q[ ] Subband 1 quantized samples Input Analysis filters (M subbands) f M Q[ ] Subband M quantized samples Psychoacoustic model Minimum masking thresholds Encoder includes a model of human perception. Candidate sets of M quantizations are tested against the model to check imperceptibility.

12 Today s lecture: Audio Coding Compression: Lossless and Lossy Quantization and Noise Psychoacoustic Masking Time-Frequency Tradeoffs Research Topics

13 The absolute threshold of hearing db Assume volume is turned up loud, so that the loudest part of the file is 96 db/spl. Intensity / db -5 db absolute threshold --- Inaudible Hz masker masked tone We are most sensitive to sounds around 3 khz. Quantization whose noise falls in an inaudible region always meets the imperceptibility constraint. 10 khz masked threshold --- Inaudible --- (log) freq

14 Tonal masking... Quantization noise will be imperceptible if it falls in the inaudible skirt surrounding a tonal signal. -- Inaudible Inaudible -- Critical band filter shape

15 Maskers compose using max() function Given a short segment of wide-band audio, we can identify narrow-band maskers and compute a composite masking function for the audio signal. Intensity / db absolute threshold -- Inaudible Hz masked tone masker Inaudible -Inaudible- -- Inaudible khz masked threshold (log) freq Effectively, tonal maskers locally raise the absolute threshold. FIGURE 35.1 Tone-on-tone simultaneous masking. A

16 Computing a mask. [1] Identify tonal (x) and non-tonal (o) energy peaks. SPL / db (analysis of a 26 ms audio frame ) x Tonal peaksx x x o o x o x x o Signal o spectrum Non-tonal energy x xx o o o o o [2] Place a local masking function for each peak. [3] Apply max() over frequency to compute the composite masker. SPL / db SPL / db x Masking spread o Resultant masking freq / Bark

17 Masking functions Masking function widens with masker level, following cochlear filter response shapes. SPL / db freq / Bark Masking function widen at higher channels, following critical bandwidth. The Bark scale warping handles this effect.

18 What is lost? Input spectrum a mono audio frame. Spectrum of encoded audio (64 kb/s). Masking profile that guided the quantization.

19 What is lost? 10 seconds of pop music content encoded using 128 kb/s. Original db RMS freq / khz kbps db RMS level / db Residual db RMS time / s Noise is only 10 to 20 db below signal... but carefully placed!

20 The bit level: An encoded frame in a file Byte index Byte (8 bits) 136 Padding to make frame an integer number of 4 byte blocks Header: defines layer, bitrate, channels, etc. (4 bytes) Subband bit allocation indices: 32 subbands x 2 channels x 4 bits = 32 bytes Subband scale factor indices: 32 subbands x 2 channels x 6 bits (only for subbands with nonzero bit allocation) 48 bytes Quantized subband samples: 32 subbands x 2 channels x 12 samples x 2-15 bits / sample (as per bit allocation, only for subbands with nonzero bit allocation) FIGURE Bit usage layout in an example MPEG-1 Audio Layer I frame encoding 384 stereo samples in 140 bytes, for a bit rate of 128 kbps. MP3: Lossless (Huffman) encoding used on sample field.

21 Today s lecture: Audio Coding Compression: Lossless and Lossy Quantization and Noise Psychoacoustic Masking Time-Frequency Tradeoffs Research Topics

22 Time-Frequency Tradeoff Good time resolution is required in the filter bank which implies a gentle rolloff in frequency.

23 Time-Frequency Tradeoff... which results in aliases appearing in sub-band outputs which fold over as we move the sub-band to baseband by downsampling. t Encoder Bandpass f Analysis Downsample M Quantize Q[ ]

24 Solution: Quadrature-mirror filter banks Input signal subband N subband N+1 Analysis f subband N out-of-band alias region f subband N+1 Downsample M Downsample M alias f nyq/m Upsample M Upsample M f nyq Reconstruct f Reconstruct f f nyq/m complementary alias + Output FIGURE 35.8 Alias cancellation in quadrature-mirror filterbanks.

25 Frame Windows Calculated mask yields imperceptible noise once the hit begins, but not during the silence before the click. Input: castanet hit. Decoder output, with artifacts. MPEG-2 pre-echo (Level II) Frame Window pre-echo 26 ms

26 MP3 solution: Variable-length frames... Input: castanet hit. MP3 Frames pre-echo Decoder output, with reduced artifacts.

27 Temporal masking effects... Masking phenomena have temporal properties which must be considered when encoding examples like castanets level / db simultaneous masking ~10 db Intensity / db backward masking ~5 ms masker envelope masked threshold forward masking ~100 ms Masking tone Elevated masking threshold skirt 15 time freq / Bark time / ms

28 Today s lecture: Audio Coding Compression: Lossless and Lossy Quantization and Noise Psychoacoustic Masking Time-Frequency Tradeoffs Research Topics

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