Audio Signal Processing Bit Allocation

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Audio Signal Processing-- Bit Allocation

• Shyh-Kang Jeng
• Department of Electrical Engineering/
• Graduate Institute of Communication Engineering

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Transform Coding Data Rates

• Encoding in frequency domain
• N equally spaced frequency bands
• Encode each band with bits
• Data rate of a critically sampling system
• Typical data rate
• from 64 kb/s/ch to 128 kb/s/ch

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Example TDAC Transform

• Sampling frequency
• Window length 1024
• Bit rate 128 kb/s/ch
• Average bits per sample
• Number of bits for each new block of data

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Uniform Quantization

• Average error of uniform quantized data
• Coding gain
• Cant gain by shifting bits away

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Bit Allocation

• Quantization using different number of bits per
  sample
• Compare various bit allocations at a given level
  of total distortion

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Floating Point Quantization

• Effect of the scale factor
• Scale to the order of the signal so that
  the error in terms of the number of mantissa bits
  
• Get coding gain if can reduce the error

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Optimal Bit Allocation

• Optimization problem
• Solution
• Lagrange multiplier
• Take derivative
• Solve for

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Optimal Bit Allocation (cont.)
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Block Uniform Allocation

• Uniform allocation of bits through block
• Optimal bit allocation
• Spectral flatness measure

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Application to Perceptual Coding

• Not to minimize the average error power
• To get the quantization noise below the masking
  curve
• To maximize SNR-SMR for signals above the masking
  curve

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Application to Perceptual Coding (cont.)

• New problem
• New solution

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A Caveat

• The above algorithm sometimes gives negative
  when is much below its geometric
  mean
• Rounds those to zero
• Take bits away from other parts of the spectrum
• Use approximate solution allocating bits one by
  one locally