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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. - PowerPoint PPT Presentation
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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
Classification of Coding schemes
Waveform coding
Vocoding
Hybrid coding
Quality versus Bitrate of Speech Codecs
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
Vocoding
‘Voice’ + ‘coding’ . Encoding information about how the speech signal
was produced by the human vocal system. These techniques can produce intelligible communi
cation at very low bit rates, usually below 4.8 kbps. However, the reproduced speech signal often sound
s quite synthetic and the speaker is often not recognisable.
LPC-10 Codec: 2400 bps American Military Standard.
Hybrid coding Combining waveform and source coding methods in
order to improve the speech quality and reduce the bitrate.
Typical bandwidth requirements lie between 4.8 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)
Quality versus Bitrate of Speech Codecs
Speech Coding (Part I) Waveform Coding
Linear PCM(Pulse-Code Modulation)
Pulse-Code Modulation (PCM)
A method for quantizing an analog signal for the purpose of transmitting or storing the signal in digital form.
Quantization
A method for quantizing an analog signal for the purpose of transmitting or storing the signal in digital form.
Linear/Uniform Quantization
Quantization Error/Noise
Quantization Error/Noise
granular noise
overloadnoise
overloadnoise
Quantization Error/Noise
Quantization Error/Noise
Unquantizedsinewave
3-bitquantizationwaveform
3-bitquantization
error
8-bitquantization
error
Quantization Step Sizemax2
2b
X
The Model of Quantization Noise
max2
2b
X Quantization Step Size
( )x n ( )x n
( ) ( ) ( )x n x n e n
2 2( )e n
+ ( )e n
+
( )x n
( )e n
( )x n
Signal-to-Quatization-Noise Ratio (SQNR)
A measurement of the effect of quantization errors introduced by analog-to-digital conversion at the ADC.
2
2-
10log signaldB
q noise
SQNR
-
20log signal
q noise
2
2-
signal
q noise
SQNR
Signal-to-Quatization-Noise Ratio (SQNR)
2
2- -
10log 20logsignal signaldB
q noise q noise
SQNR
( ) ( ) ( )x n x n e n 2 2( )e n max2
2b
X
2 2( ) ~ ( , )e n U 2
2
12e Assume
2max
23 2 b
X
2
210log x
dBe
SQNR
max10log3 20 log 2 20logx
Xb
max4.77 6.02 20logx
Xb
2
2
max
3 210log
b
xX
Signal-to-Quatization-Noise Ratio (SQNR)
2
2- -
10log 20logsignal signaldB
q noise q noise
SQNR
( ) ( ) ( )x n x n e n 2 2( )e n max2
2b
X
2 2( ) ~ ( , )e n U 2
2
12e Assume
2max
23 2 b
X
2
210log x
dBe
SQNR
max10log3 20 log 2 20logx
Xb
max4.77 6.02 20logx
Xb
2
2
max
3 210log
b
xX
Is the assumption always
appropriate? Is the assumption always
appropriate?
Signal-to-Quatization-Noise Ratio (SQNR)
max4.77 6.02 20logdBx
XSQNR b
constantconstant
Each code bit contributes
6dB.
Each code bit contributes
6dB.
The term Xmax/x tells howbig a signal can be
accurately represented
The term Xmax/x tells howbig a signal can be
accurately represented
2
2- -
10log 20logsignal signaldB
q noise q noise
SQNR
Signal-to-Quatization-Noise Ratio (SQNR)
max4.77 6.02 20logdBx
XSQNR b
Depending on the distribution of signal, which,
in turn, depends on users and time.
Depending on the distribution of signal, which,
in turn, depends on users and time.
Determined by A/D converter.
Determined by A/D converter.
2
2- -
10log 20logsignal signaldB
q noise q noise
SQNR
Signal-to-Quatization-Noise Ratio (SQNR)
max4.77 6.02 20logdBx
XSQNR b
In what condition, the formula is reasonable?
In what condition, the formula is reasonable?
2
2- -
10log 20logsignal signaldB
q noise q noise
SQNR
Overload Distortion
maxXmaxX
midtread
maxXmaxX
midrise
Probability of Distortion
maxXmaxX
midtread
maxXmaxX
midrise
xx
Assume 2~ (0, )xx N
Probability of Distortion
maxXmaxX
midtread
maxXmaxX
midrise
xx
Assume 2~ (0, )xx N
max(" ")x
XP overlad
max(" ")x
XP overlad
max 3
(" ") 0.0026xX
P overlad
max 3
(" ") 0.0026xX
P overlad
Overload and Quantization Noise withGaussian Input pdf and b=4
maxXmaxX
midtread
maxXmaxX
midrise
xx
Assume 2~ (0, )xx N
max ( )xX dB
( )e
dB
Uniform Quantizer Performance
max ( )xX dB
( )
SQNR
dB
Uniform Input Pdf
max ( )xX dB
( )
SQNR
dB
Gaussian Input Pdf
More on Uniform Quantization
max4.77 6.02 20logx
XSQNR b
Conceptually and implementationally simple.– Imposes no restrictions on signal's statistics– Maintains a constant maximum error across its total dynam
ic range. x varies so much (order of 40 dB) across sounds, spe
akers, and input conditions. We need a quantizing system where the SQNR is ind
ependent of the signal’s dynamic range, i.e., a near-constant SQNR across its dynamic range.
Speech Coding (Part I) Waveform Coding
Nonlinear PCM
Probability Density Functionsof Speech Signals
Counting the number of samples in each interval provides an estimate of the pdf of the signal.
Probability Density Functionsof Speech Signals
Probability Density Functionsof Speech Signals
Good approx. is a gamma distribution, of the form
Simpler approx. is a Laplacian density, of the form:
1/ 2 3| |
23( )
8 | |x
x
x
p x ex
(0)p
2| |1
( )2
x
x
x
p x e
1(0)
2 x
p
Probability Density Functionsof Speech Signals
Distribution normalized so that x=0 and x=1•
Gamma density more closely approximates measured distribution for speech than Laplacian.
Laplacian is still a good model in analytical studies.
Small amplitudes much more likely than large amplitudes—by 100:1 ratio.
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.
Companding
Compressor ExpanderUniformQuantizer
( )C x 1( )C xx xy y
( )g x 1( )g xx xy y
Companding
Compressor ExpanderUniformQuantizer
( )g x 1( )g xx xy y
Companding
Compressor ExpanderUniformQuantizer
After compression, y is
Nearly uniformly distributed
( )g x 1( )g xx xy y
The Quantization-Error Variance of Nonuniform Quantizer
Compressor ExpanderUniformQuantizer
max
max
22
2
( )
12 ( )
X
e X
p xdx
C x
Jayant and Noll
( )g x 1( )g xx xy y
The Quantization-Error Variance of Nonuniform Quantizer
Compressor ExpanderUniformQuantizer
Jayant and Nollmax
max
22
2
( )
12 ( )
X
e X
p xdx
C x
( )g x 1( )g xx xy y
The Optimal C(x)
Compressor ExpanderUniformQuantizer
max
max
22
2
( )
12 ( )
X
e X
p xdx
C x
Jayant and Noll
If the signal’s pdf is known, then the minimum SQNR, is achievable by letting
max
3
0max
3
0
( )( )
( )
x
X
p x dxC x X
p x dx
( )g x 1( )g xx xy y
The Optimal C(x)
Compressor ExpanderUniformQuantizer
max
max
22
2
( )
12 ( )
X
e X
p xdx
C x
Jayant and Noll
If the signal’s pdf is known, then the minimum SQNR, is achievable by letting
max
3
0max
3
0
( )( )
( )
x
X
p x dxC x X
p x dx
Is the assumption realistic.Is the assumption realistic.
PDF-Independent Nonuniform Quantization
2
2x
e
SQNR
max
max
max
max
2
2
2
( )
1( )
12 ( )
X
X
X
X
x p x dx
p x dxC x
Assuming overload free,
We require that SQNR is independent on p(x).
22 2
1 1
( )x
kC x
( ) /C x k x ( ) lnC x k x A
Logarithmic Companding
( ) lnC x k x A
-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
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
-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
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
( )y C x ln 1 | |
( )ln(1 )
xsign x
( )Ay C x
| | 1( ) 0 | |
1 ln1 ln | | 1
( ) | | 11 ln
A xsign x x
A AA x
sign x xA A
(=255 in U.S. and Canada)
(A=87.56 in Europe)
-Law & A-Law Companding
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
x
()
yC
x
x
()
Ay
Cx
( )x nmaxX
maxX
()
yC
x
-Law Companding
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
( )y C x maxmax
| |ln 1
( )ln(1 )
xX
X sign x
( ) 0 ( ) 0x n y n
max max( ) ( )x n X y n X
0 ( ) ( )y n x n
( )x nmaxX
maxX
()
yC
x
-Law Companding
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
( )y C x maxmax
| |ln 1
( )ln(1 )
xX
X sign x
maxmax max
maxmax max
1 | | | |( ) 1
ln( )
1 | | | |ln ( ) 1
ln
x xX sign x
X XC x
x xX sign x
X X
1ln 1
ln 1
z zz
z z
1ln 1
ln 1
z zz
z z
( )x nmaxX
maxX
()
yC
x
-Law Companding
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
( )y C x maxmax
| |ln 1
( )ln(1 )
xX
X sign x
1ln 1
ln 1
z zz
z z
1ln 1
ln 1
z zz
z z
LinearLinear
LogLog
maxmax max
maxmax max
1 | | | |( ) 1
ln( )
1 | | | |ln ( ) 1
ln
x xX sign x
X XC x
x xX sign x
X X
Histogram for -Law Companding
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
x(n)
y(n)
-law Approximation to Log
( )g x 1( )g xx xy y( )g x 1( )g xx xy y
Compressor ExpanderUnif ormQuantizer
( )x n
ˆ( )y n
Distribution of quantization level for a -law
3-bit quantizer.
SQNR of -law Quantizer
6.02b dependence on b
Much less dependence on Xmax/x
For large SQNR is less sensitive to the changes in Xmax/x
2
max max6.02 4.77 20log ln(1 ) 10log 1 2dBx x
X XSQNR b
good
good
good
Comparison of Linear and -law Quantizers
2
max max6.02 4.77 20log ln(1 ) 10log 1 2dBx x
X XSQNR b
max6.02 4.77 20logdBx
XSQNR b
Linear
A-Law Companding
( )Ay C x
| | 1( ) 0 | |
1 ln1 ln | | 1
( ) | | 11 ln
A xsign x x
A AA x
sign x xA A
A-Law Companding
( )Ay C x
| | 1( ) 0 | |
1 ln1 ln | | 1
( ) | | 11 ln
A xsign x x
A AA x
sign x xA A
LinearLinear
LogLog
A-Law Companding
x
()
yC
x
x
()
Ay
Cx
SQNR of A-Law Companding
6.02 4.77 20log(1 )dBSQNR b A
Demonstration
PCM Demo
Speech Coding (Part I) Waveform Coding
Max-Lloyd Algorithm
How to design a nonuniform quantizer?
xkxk1 xk+1
ck
ck1
x
Q(x)
qk1
qk
Q(x): Quantization (Reconstruction) Level
1k k kx q x
qk
How to design a nonuniform quantizer?
xkxk1 xk+1
ck
ck1
x
Q(x)
qk1
qk
Q(x): Quantization (Reconstruction) Level
1k k kx q x
qk
? ?
?
How to design a nonuniform quantizer?
ck ck+1 ck+2 ck+3ck1ck2
xk xk+1 xk+2 xk+3xk1xk2 xk+4
qk qk+1 qk+2 qk+3qk1qk2
Major tasks:1. Determine the decision thresholds xk’s2. Determine the reconstruction levels qk’s
Related task:3. Determine codewords ck’s
Optimal Nonuniform Quantization
22 ( )e E X Q X
An optimal quantizer is the one that minimizes the following quantization-error variance.
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
Major tasks:1. Determine the decision thresholds xk’s2. Determine the reconstruction levels qk’s
Optimal Nonuniform Quantization
22 ( )e E X Q X
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
1 2
1
( )k
k
N x
kxk
x q p x dx
2( ) ( )e x p x dx
1
1
1
2* * * *1 1
1
( , , , , , ) arg min ( )k
kN
N
N x
N N kxx x kq q
x x q q x q p x dx
Necessary Conditions for an Optimum
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
1
1
1
2* * * *1 1
1
( , , , , , ) arg min ( )k
kN
N
N x
N N kxx x kq q
x x q q x q p x dx
2e
2
0e
kq
2
0e
kx
leads to the “centroid” condition
leads to the “nearest neighborhood” condition
Necessary Conditions for an Optimum
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
2
0e
kq
2
0e
kx
leads to the “centroid” condition
leads to the “nearest neighborhood” condition
1
1
( ), 1, ,
( )
k
k
k
k
x
xk x
x
xp x dxq k N
p x dx
1 , 1, ,2
k kk
q qx k N
Optimal Nonuniform Quantization
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
2
0e
kq
2
0e
kx
leads to the “centroid” condition
leads to the “nearest neighborhood” condition
1
1
( ), 1, ,
( )
k
k
k
k
x
xk x
x
xp x dxq k N
p x dx
1 , 1, ,2
k kk
q qx k N
This suggests an
iterative algorithm to
reach the optimum.
This suggests an
iterative algorithm to
reach the optimum.
The Max-Lloyd algorithm
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
1. Initialize a set of decision levels {xk} and set
2. Calculate reconstruction levels {qk} by
3. Calulate mse by
4. If , exit.5. Set and adjust decision levels {xk} by
6. Go to 2
1
1
( )
( )
k
k
k
k
x
x
x
x
kqxp x dx
p x dx
1
2kk kq q
x
1 22 ( )k
k
x
ke xx p x dxq
2e
2 2e e
2 2e e
The Max-Lloyd algorithm
ck ck+1 ck+2 ck+3ck 1ck 2 ck ck+1 ck+2 ck+3ck 1ck 2
xk xk+1 xk+2 xk+3xk 1xk 2 xk+4xk xk+1 xk+2 xk+3xk 1xk 2 xk+4
qk qk+1 qk+2 qk+3qk 1qk 2 qk qk+1 qk+2 qk+3qk 1qk 2
1. Initialize a set of decision levels {xk} and set
2. Calculate reconstruction levels {qk} by
3. Calulate mse by
4. If , exit.5. Set and adjust decision levels {xk} by
6. Go to 2
1
1
( )
( )
k
k
k
k
x
x
x
x
kqxp x dx
p x dx
1
2kk kq q
x
1 22 ( )k
k
x
ke xx p x dxq
2e
2 2e e
2 2e e
This version assumes that the pdf of signa
l is availabe.This version assumes that the pdf of signa
l is availabe.
The Max-Lloyd algorithm(Practical Version)
Exercise
Speech Coding (Part I) Waveform Coding
Differential PCM (DPCM)
Typical Audio Signals
0 500 1000 1500 2000 2500-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
A segment of audio signals
Do you find any correlation and/or redundancy among the samples?
The Basic Idea of DPCM
Adjacent samples exhibit a high degree of correlation.
Removing this adjacent redundancy before encoding, a more efficient coded signal can be resulted.
How?– Accompanying with prediction (e.g., linear prediction)– Encoding prediction error only
Linear Prediction
1
ˆ( ) ( )p
kk
s n a s n k
ˆ( ) ( ) ( )e n s n s n
1
( ) ( )p
kk
s n a s n k
n1
n2
n3
np
ˆ( )s n
( )s n
2
1
( )N
pn
e n
E* arg min p
aa E
1( , , )pa a a
Linear Predictor
1
ˆ( ) ( )p
kk
s n a s n k
PredictorPredictor( )s n ˆ( )s n
DPCM Codec
ˆ( )s n
QuantizerQuantizer( )e n
( )s n
( )e n( )s n
PredictorPredictor
( )e n
+
++
( )s n
PredictorPredictor( )s n ˆ( )s nPredictorPredictorPredictorPredictor( )s n ˆ( )s n
ˆ( )s n
Channel
Channel( )e n
PredictorPredictor
( )s n+
+
ˆ( )s n
( )s nA/D
converter
DPCM Codec
Channel( )e n
PredictorPredictor
( )s n+
+
ˆ( )s n
ˆ( )s n
QuantizerQuantizer( )e n
( )s n
( )e n( )s n
PredictorPredictor
( )e n
+
++
( )s n
ˆ( )s n
Channel
( )s nA/D
converter
The dynamic range of prediction
error is much smaller than the
signal’s.
Less quantization levels needed
The dynamic range of prediction
error is much smaller than the
signal’s.
Less quantization levels needed
Performance of DPCM
By using a logarithmic compressor and a 4-bit quantizer for the error sequence e(n), DPCM results in high-quality speech at a rate of 32,000 bps, which is a factor of two lower than logarithmic PCM
Speech Coding (Part I) Waveform Coding
Adaptive PCM (ADPCM)
Basic Concept The power level in a speech signal varies
slowly with time.
Let the quantization step dynamically adapt to the slowly time-variant power level.
(n)
( ) ( )n n
Adaptive Quantization Schemes
Feed-forward-adaptive quantizers– estimate (n) from x(n) itself– step size must be transmitted
Feedback-adaptive quantizers– adapt the step size, , on the basis of the quantize
d signal– step size needs not to be transmitted
ˆ( )x n
Feed Forward Adaptation
QuantizerQuantizer EncoderEncoder
Step-SizeAdaptation
System
Step-SizeAdaptation
System
( )x n ˆ( )x n ( )c n
( )n( )n
DecoderDecoder( )c n
( )n
ˆ( )x n
Feed Forward Adaptation
QuantizerQuantizer EncoderEncoder
Step-SizeAdaptation
System
Step-SizeAdaptation
System
( )x n ˆ( )x n ( )c n
( )n( )n
DecoderDecoder( )c n
( )n
ˆ( )x n
The source signal is not available at receiver. So, the receiver can’t evaluate (n) by itself.
The source signal is not available at receiver. So, the receiver can’t evaluate (n) by itself.
(n) has to be transmitted.
( )x n
Quantization errorˆ( ) ( ) ( )e n x n x n
QuantizerQuantizer EncoderEncoder( )x n ˆ( )x n ( )c n
( )n( )n
DecoderDecoder( )c n
( )n
ˆ( )x n ( )x n
Quantization errorˆ( ) ( ) ( )e n x n x n
(n) has to be transmitted.
The Step-Size Adaptation System
Step-SizeAdaptation
System
Step-SizeAdaptation
System
Estimate signal’s short-time energy,
2(n), and make (n) (n).
Estimate signal’s short-time energy,
2(n), and make (n) (n).
0( ) ( )n n
The Step-Size Adaptation System Low-Pass Filter Approach
2 2( ) ( ) ( )n
m
n x m h n m
( ) , 0,0 1nh n n
2 ( )n
n m
m
x m
1
2 2( ) ( )n
n m
m
x m x n
1
1 2 2( ) ( )n
n m
m
x m x n
2 2( 1) ( )n x n
0( ) ( )n n
The Step-Size Adaptation System Low-Pass Filter Approach
0( ) ( )n n
= 0.99 = 0.9
The Step-Size Adaptation System Moving Average Approach
2 2
1
( ) ( ) ( )n
m n M
n x m h n m
1( ) ,0 0 1h n M
M
2
1
1( )
n
m n M
x mM
0( ) ( )n n
Feed-Forward Quantizer
2 2
1
1( ) ( )
n
m n M
n x mM
0( ) ( )n n
(n) evaluated every M Samples Use M=128, 1024 for estimates Suitable choosing of min and max
Feed-Forward Quantizer
2 2
1
1( ) ( )
n
m n M
n x mM
0( ) ( )n n
(n) evaluated every M Samples Use M=128, 1024 for estimates Suitable choosing of min and max
Too longToo long
Feedback Adaptation
QuantizerQuantizer EncoderEncoder
Step-SizeAdaptation
System
Step-SizeAdaptation
System
( )x n ˆ( )x n ( )c n
( )n
DecoderDecoder( )c n
( )n
ˆ( )x n
Step-SizeAdaptation
System
Step-SizeAdaptation
System
(n) can be evaluated at both sides using the same alogorithm. Hence, it needs not to be transmitted.
The Step-Size Adaptation System
Step-SizeAdaptation
System
Step-SizeAdaptation
System
Step-SizeAdaptation
System
Step-SizeAdaptation
System
QuantizerQuantizer EncoderEncoder( )x n ˆ( )x n ( )c n
( )n
DecoderDecoder( )c n
( )n
ˆ( )x n
The same as feed-forward adaptation except that the input changes.
Alternative Approach to Adaptation
( ) ( ) ( 1)n P n n
P(n){P1, P2, …} depends on c(n1).
Needs to impose the limits
The ratio max/min controls the dyna
mic range of the quantizer.
min max( )n
Alternative Approach to Adaptation
( ) ( ) ( 1)n P n n
P(n){P1, P2, …} depends on c(n1).
Needs to impose the limits
The ratio max/min controls the dyna
mic range of the quantizer.
min max( )n P1
P2
P3
P4
P5
P6
P7
P8
Alternative Approach to Adaptation
Speech Coding (Part I) Waveform Coding
Delta Modulation
(DM)
Delta Modulation
Simplest form of DPCM– The prediction of the next is simply the current
Sampling rate chosen to be many times (e.g., 5) the Nyquist rate, adjacent samples are quite correlated, i.e., s(n)s(n1).– 1-bit (2-level) quantizer is used– Bit-rate = sampling rate
Review DPCM
ˆ( )s n
QuantizerQuantizer( )e n
( )s n
( )e n( )s n
PredictorPredictor
( )e n
+
++
( )s n
ˆ( )s n
Channel
Channel( )e n
PredictorPredictor
( )s n+
+
ˆ( )s n
( )s nA/D
converter
DM Codec
ˆ( )s n
QuantizerQuantizer( )e n
( )s n
( )s n
PredictorPredictor
( )e n
+
++
Channel
1
Channel( )e n
PredictorPredictor
( )s n+
+
ˆ( )s n
A/Dconverter
z1
z1
time
Distortions of DM
0 1 1 1 1 1 0 0 0 0 1 0 0 1 0
T
step size
code words:1 ( ) 1
( )0 ( ) 1
e nc n
e n
time
Distortions of DM
0 1 1 1 1 1 0 0 0 0 1 0 0 1 0
T
step size
code words:1 ( ) 1
( )0 ( ) 1
e nc n
e n
granular noisegranular noise
slope overload condition
slope overload condition
time
Choosing of Step Size
Needs small step size
Needs small step size
Needs large step size
Needs large step size
time
Adaptive DM (ADM)
( ) ( 1)( ) ( 1) e n e nn n K
Adaptive DM (ADM)
( ) ( 1)( ) ( 1) e n e nn n K
2K