1. Speech & Audio Processing
Digital Systems: Hardware Organization and Design
4/16/2017
Speech & Audio Processing
Speech & Audio Coding Examples
Architecture of a Respresentative 32 Bit Processor

2. Linear Prediction Analysis
A Simple Speech Coder
LPC Based Analysis Structure
Linear Prediction Analysis
Pre- emphasis
Windowing Analysis
Auto- Correlation
Levinson- Durbin
Audio Input
Residual
Residual
Analysis Filter
Quantization
Filter Coeffs
Filter Coeffs
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3. Windowing Analysis Stage
N – Length of the Analysis Window
10-30 msec
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4. Some Analysis Windows
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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
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6. LPC Analysis Stage LPC Method Described in: Summary: MATLAB help
Ch5-Analysis_&_Synthesis_of_Pole-Zero_Speech_Models.ppt
Summary:
Perform Autocorrelation
Solve system of equations with Durbin-Levinson Method
MATLAB help
doc lpc, etc.
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7. Example of MATLAB Code ge[n] ŝ[n] function myLPCCodec(wavfile, N) %
% wavfile - input MS wav file
% N LPC Filter Order
[x, fs, nbits] = wavread(wavfile);
% plot(x);
% Playing Original Signal
soundsc(x,fs);
% Performing LPC analysis using MATLAB lpc function
[a, g] = lpc(x,N);
% performing filtering operation on estimated filter coeffs
% producing predicted samples
est_x = filter([0 -a(2:end)], 1, x);
% error signal
e = x - est_x;
% Testing the quality of predicted samples
soundsc(est_x, fs);
% Synthesis Stage With Zero Loss of Information
syn_x = filter([0 -a(2:end)], 1, g.*e);
soundsc(syn_x,fs);
ge[n]
ŝ[n]
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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.
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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.
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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:
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11. PARCOR Filter Representation
PARCOR to LPC:
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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
Ap-1 A0 + + kp
kp-1
kp+1=∓1
k0=-1 - -
z-1
z-1
z-1 Bp
Bp-1 B0
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13. Line Spectral Frequency Representation
From previous slide the following holds:
From this realization of the filter the LSP representation is derived:
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14. LSF Representation
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15. LPC Synthesis Filter with LSF
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16. A Simple Speech Coder LPC Based Synthesis Structure Residual Signal
Synthesis Filter
De- emphasis
Audio Output
Residual
Decoding
Filter Coeffs
Filter Coeffs
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17. Audio Coding

18. Digital Systems: Hardware Organization and Design
4/16/2017
Audio Coding
Most of the Audio Coding Standards use principles of Psychoacoustics.
Example of Basic Structure of MP3 encoder:
Audio Input
Bit-stream
Filterbank & Transform
Quantization
Psychoacoustic Model
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Architecture of a Respresentative 32 Bit Processor

19. Basic Structure of Audio Coders
Filterbank Processing
Psychoacoustic Model
Quantization
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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
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22. Mel-Scale
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23. Bark-Scale
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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
Audio Input
Bit Stream
hk[n] ↓
MDCT
Quantization
MDCT
hN[n] ↓
MDCT
MDCT
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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
MDCT
IMDCT ↑
g1[n]
Bit Stream
Audio Output
Decoding
MDCT
IMDCT ↑
gk[n]
MDCT
IMDCT ↑
gN[n]
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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.
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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.
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29. Threshold of Hearing
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30. Frequency Masking Schröder Spreading Function Bark Scale Function:
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31. Masking Curve
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32. Primary Tone 1kHz
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33. Masked Tone 900 Hz
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34. Combined Sound 1kHz + 0.9kHz
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35. Combined 1kHz + 0.9kHz (-10dB)
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36. Combined 1kHz + 5kHz (-10dB)
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37. END
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