adaptive filters adaptation control - dss.tf.uni-kiel.de · slide 1 gerhard schmidt...
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Slide 1
Gerhard Schmidt
Christian-Albrechts-Universität zu KielFaculty of Engineering Electrical Engineering and Information TechnologyDigital Signal Processing and System Theory
Adaptive Filters – Adaptation Control
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Slide 2Slide 2Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Today:
Contents of the Lecture
Adaptation Control:
Introduction and Motivation
Prediction of the System Distance
Optimum Control Parameters
Estimation Schemes
Examples
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Slide 3Slide 3Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Basics
Application Example – Echo Cancellation
e(n)+
x(n)
d(n)
s(n)
b(n)bd(n)
Echocancel-lationfilter
y(n)
e(n)+
x(n)
bd(n)
y(n)
+ +
s(n)
b(n)
Application example:
Model:
d(n)
Objective:Remove those components in the microphonesignal that originate from the remote communication partner!
h(n)
bh(n)
x(n)
bh(n)
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Slide 4Slide 4Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Basic Approach
Application Example – Echo Cancellation
Cancelling acoustic echoes by means of an adaptive filter with coefficients, operating at a sample rate kHz. For the adaptation of the filter the NLMS algorithm should be used.
Model:The loudspeaker-enclosure-microphone (LEM) system is modelled as a linear (only slowly changing)system with finite memory.
Approach:
Advantages and disadvantages:
In contrast to former approaches (loss controls) simultaneous speech activity in both communication directions is possible now.
The NLMS algorithm is a robust and computationally efficient approach.
Compared to former solutions more memory and a larger computational load are required.
Stability can not be guaranteed.
+
+_
_
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Slide 5Slide 5Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
NLMS-Algorithm
Application Example – Echo Cancellation
Computation of the error signal (output signal of the echo cancellation filter):
Recursive computation of the norm of the excitation signal vector
Adaptation of the filter vector:
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Slide 6Slide 6Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Convergence Examples – Part 1
Application Example – Echo Cancellation
Convergence withoutbackground noiseand without localspeech signals
Excitation signal
Local signal
Error signal
Microphone and error power
Time in seconds
Microphone signal
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Slide 7Slide 7Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Convergence Examples – Part 2
Application Example – Echo Cancellation
Excitation signal
Local signal
Error signal
Microphone and error power
Time in seconds
Microphone signal
Convergence withbackground noisebut without localspeech signals
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Slide 8Slide 8Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Convergence Examples – Part 3
Application Example – Echo Cancellation
Excitation signal
Local signal
Error signal
Microphone and error power
Time in seconds
Microphone signal
Convergence withoutbackground noisebut with localspeech signals
(step size = 1)
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Slide 9Slide 9Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Convergence Examples – Part 4
Application Example – Echo Cancellation
Excitation signal
Local signal
Error signal
Microphone and error power
Time in seconds
Microphone signal
Convergence withoutbackground noisebut with localspeech signals
(step size = 0.1)
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Slide 10Slide 10Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Literature
Adaptation Control
E. Hänsler / G. Schmidt: Acoustic Echo and Noise Control – Chapter 7 (Algorithms for Adaptive Filters), Wiley, 2004
E. Hänsler / G. Schmidt: Acoustic Echo and Noise Control – Chapter 13 (Control of Echo Cancellation Systems), Wiley, 2004
Basic texts:
Further details:
S. Haykin: Adaptive Filter Theory – Chapter 6 (Normalized Least-Mean-Square Adaptive Filters), Prentice Hall, 2002
C. Breining, A. Mader: Intelligent Control Strategies for Hands-Free Telephones, in E. Hänsler, G. Schmidt, Topics on Acoustic Echo and Noise Control – Chapter 8, Springer, 2006
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Slide 11Slide 11Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Control Approaches – Part 1
Adaptation Control
Scalar control approach:
Vector control approach:
Regularization
Step size
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Slide 12Slide 12Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Control Approaches – Part 2
Adaptation Control
Example for asparse impulseresponse
For such systemsa vector basedcontrol schemecan be advantageous.
Impulse response of the system to be identified
Example for a vector step size
Coefficient index i
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Slide 13Slide 13Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
How do we go on …
Adaptation Control
Problem(echo cancellation performanceduring „double talk“)
Analysis of the average system distance(taking local signals into account)
Derivation of an optimal step size(using non-measurable signals)
Estimation of the non-measurable signalcomponents (leads to an implementable control scheme)
Solution(robust echo cancellation due to step-size control)
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Slide 14Slide 14Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Average System Distance – Part 1
Adaptation Control
Adaptation using the NLMS algorithm (only step-size controlled) :
Assumptions:
White noise as excitation and (stationary) distortion:
Statistical independence between filter vectorand excitation vector.
Definition of the average system distance:
e(n)+
x(n)
bd(n)
y(n)
+
s(n)
b(n)
d(n)
bh(n)
+n(n)
h Time-invariant system:
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Slide 15Slide 15Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Average System Distance – Part 2
Adaptation Control
… Derivation during the lecture …
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Slide 16Slide 16Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Average System Distance – Part 3
Adaptation Control
Contraction parameter Expansion parameter
Generic approach (control scheme with step size and regularization):
Result:
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Slide 17Slide 17Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Contraction and Expansion Parameters
Adaptation Control
Contraction parameter :
Range:
Desired: as small as possible
Determines the speed of convergence without distortions
Expansion parameter :
Range:
Desired: as small as possible
Determines the robustness against distortions
Opposite to eachother – a commonsolution (optimization)Has to be found!
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Slide 18Slide 18Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Influence of the Control Parameters
Adaptation Control
Values for thecontraction and expansion parametersfor the conditions:
Contraction parameter
Step size
Step
siz
e
Step size Regularization
RegularizationRegularization
Step
siz
e
Expansion parameter
Regularization
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Slide 19Slide 19Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
True and Prediction System Distance
Adaptation Control
Boundary conditions of the simulation:
Excitation: white noise
Distortion: white noise
SNR: 30 dB
System distance
Distortion
Excitation
Iterations
(Simulation)(Simulation)
(Theory)(Theory)
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Slide 20Slide 20Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Maximum Convergence Speed – Part 1
Adaptation Control
For the special case without any distortions
and with optimal control parameters for that case
we get
Meaning that the average system distance can be reduced per adaptation step by a factor of. As a result adaptive filters with a lower amount of coefficients converge faster
than long adaptive filters.
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Slide 21Slide 21Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Maximum Convergence Speed – Part 2
Adaptation Control
If we want to know how long it takes to improve the filter convergence by 10 dB, we can make the following ansatz:
As on the previous slide we assumed an undisturbed adaptation process. By applying the natural logarithm we obtain
By using the following approximations
for and
we get
This means: At maximum speed of convergence it takes about 2N iterations until the average systemdistance is reduced by 10 dB.
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Slide 22Slide 22Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
The „10 dB per 2N“ Rule
Adaptation Control
Boundary conditions ofthe simulation: Excitation: white noise
Distortion: white noise
SNR: 30 dB
Step size: 1
Different filter lengths (500 and 1000)
Average system distance
Average system distance
Iterations
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Slide 23Slide 23Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Prediction of the Steady-State Convergence – Part 1
Adaptation Control
Recursion of the average system distance:
For and appropriately chosen control parameters we obtain:
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Slide 24Slide 24Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Prediction of the Steady-State Convergence – Part 2
Adaptation Control
By inserting the results from the previous slide we obtain:
For the adaptation without regularization we get:
Inserting these values leads to:
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Slide 25Slide 25Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Optimal Step Size – Motivation
Adaptation Control
Remarks:
With a large step sizeone can achieve a fast initial convergence, but only a poor steady-state performance.
With a small step size a good steady-state performance can be obtained, but only a slow initial convergence.
Solution:
Utilization of a time-variant step-size.
Estimated speedof convergence
Average system distance (only step-size control)
Estimated steady-state performance
Iterations
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Slide 26Slide 26Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Optimal Step Size – Derivation
Adaptation Control
… Derivation during the lecture …
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Slide 27Slide 27Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Optimal Step Size – Example
Adaptation Control
Computation of the step size:
with
Boundary conditions of the simulation:
Excitation: white noise
Distortion: white noise
SNR: 30 dB
Filter length: 1000 coefficients
Iterations
Time-variant step size
Average system distance
Step size = 1Step size = 0.5Step size = 0.25
Time-variant step size
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Slide 28Slide 28Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation of the Optimal Step Size
Adaptation Control
Approximation for the optimal step size:
For white excitation we get:
Ansatz:
Short-term power of the error signal
Short-term power of the excitation signal
Estimated system distance
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Slide 29Slide 29Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation Procedures for the Optimal Step Size – Part 1
Adaptation Control
First order IIR smoothing with different time constants for rising and falling signal edges:
Basic structure:Different time constants are used to achieve smoothing on one hand but also being able to
follow sudden signal increments quickly on the other hand.
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Slide 30Slide 30Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation Procedures for the Optimal Step Size – Part 2
Adaptation Control
Boundary conditions of the simulation:
Excitation: speech
SNR: about 20 dB
Sample rate: 8 kHz
¯r = 0:007; ¯f = 0:002
Microphone signal
Estimated short-term power
Time in seconds
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Slide 31Slide 31Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation Procedures for the Optimal Step Size – Part 3
Adaptation Control
Estimating the system distance:
Problem:
The coefficients are not known.
Solution:
We extend the system by an artificial delay of samples. For that part of the impulse response we have
With these so-called delay coefficients we can extrapolate the system distance:
for
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Slide 32Slide 32Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation Procedures for the Optimal Step Size – Part 4
Adaptation Control
Structure of the system distance estimation:
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Slide 33Slide 33Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation Procedures for the Optimal Step Size – Part 5
Adaptation Control
„Error spreading property“of the NLMS algorithm:
System error vector (magnitudes) after 0 iterations
System error vector (magnitudes) after 500 iterations
System error vector (magnitudes) after 2000 iterations
System error vector (magnitudes) after 4000 iterations
Coefficient index
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Slide 34Slide 34Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Estimation Procedures for the Optimal Step Size – Part 6
Adaptation Control
Boundary conditions of thesimulation:
Excitation: speech
Distortion: speech
SNR during single talk: 30 dB
Filter length: 1000 coefficients
Excitation signal
Local speech signal
Measured and estimated system distance
Step size
Iterations
Measured system distanceEstimated system distance
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Slide 35Slide 35Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Convergence Examples – Part 5
Application Example – Echo Cancellation
For Comparison:
Fixed step size 1.0
Fixed step size 0.1
Excitation signal
Local signal
Error signal
Microphone and error power
Time in seconds
Microphone signal
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Slide 36Slide 36Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Convergence Examples – Part 6
Application Example – Echo Cancellation
Controlled step size
Fixed step size (0.1)
Fixed step size (1.0)
Microphone signal
Time in seconds
Short-term microphone and error power
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Slide 37Slide 37Digital Signal Processing and System Theory| Adaptive Filters | Adaptation Control
Summary and Outlook
Adaptive Filters – Adaptation Control
This week:
Introduction and Motivation
Prediction of the System Distance
Optimum Control Parameters
Estimation Schemes
Examples
Next week:
Reducing the Computational Complexity of Adaptive Filters