m ila g orobets enel 667 - i ntelligent control - c ourse p resentation a pril 7, 2014
TRANSCRIPT
INTELLIGENT CONTROL
APPROACH TO THE COCKTAIL PARTY
PROBLEM
MILA GOROBETS
ENEL 667 - INTELLIGENT CONTROL - COURSE PRESENTATION
APRIL 7, 2014
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OUTLINE What is the cocktail party problem? Selected approach and Results
Blind Signal Separation
Signal Prediction
Cancellation of Contaminants
Other Applications Conclusions Future Work
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COCKTAIL PARTY PROBLEM Most humans can select a single voice or sound from a
mixture Tuning into one sound Tuning out everything else
Works best with two ears [1] Related to localization
Can we get a computer to do this reliably?
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APPROACH OVERVIEW Use 3 neural networks to separate and predict two different signals One signal is cancelled Assume stereo signals are available (but sources can be close)
Stereo Signal Mixing
Blind Signal Separation
Prediction of Signal 1
Prediction of Signal 2
Separated Signal 1
Separated Signal 2
Signal arriving at Ear 1
Signal arriving at Ear 2
Signal 1 at Ear 1+
+ Signal 1 at Ear 2
Signal 1
Signal 2 Neural Network 1
Neural Network 2
Neural Network 3
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1. BLIND SIGNAL SEPARATION Two measurement sources (microphones) Recursive update [2] Performs decorrelation
+
+
Signal at Ear 1
Signal at Ear 2
Separated Signal 2
Separated Signal 11/(1-C11C21)
Z-11/(1-C11C21) Z-1 Z-1 Z-1
Z-11/(1-C11C21) Z-1 Z-1 Z-1
-C11
C21
1/(1-C11C21)
C11
C22
+
C23 C24 C2n
-C12 -C13 -C14 -C1m
-C22 -C23 -C24 -C2n
C21 +
C12 C13 C14 C1m
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1. BLIND SIGNAL SEPARATION (CONT’D) Signals are separated with MSE
below 1e-3 Problems occur when:
Stereo deteriorates (-> mono) Signals are of similar frequencies
8.3 8.31 8.32 8.33 8.34 8.35 8.36 8.37 8.38 8.39 8.4
x 104
-0.5
0
0.5Desired Signal
Sample number
Am
plitu
de (
V)
Separated signal Desired Signal Error
8.3 8.31 8.32 8.33 8.34 8.35 8.36 8.37 8.38 8.39 8.4
x 104
-0.5
0
0.5Contaminating Signal
Sample number
Am
plitu
de (
V)
Separated signal Desired Signal Error
8.3 8.31 8.32 8.33 8.34 8.35 8.36 8.37 8.38 8.39 8.4
x 104
-0.5
0
0.5
1
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1. BLIND SIGNAL SEPARATION (CONT’D) The separator acts as a filter that passes the desired signal
Simple Case: Voice + Alarm
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2. SIGNAL PREDICTION Multilayer Perceptron
Input, hidden and output layers Hyperbolic tangent activation functions
Learning using Backpropagation [3] Hybrid [3,4]
Backpropagation for Outer Layer
Recursive Least Squares for Inner Layer
Control System to Enhance Output
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2. SIGNAL PREDICTION STABILITY Hybrid updating
Lyapunov candidate function:
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2. SIGNAL PREDICTION STABILITY Taylor Series Expansions, substitutions, etc give the following:
Leading to update equations:
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2. SIGNAL PREDICTION STABILITY Substitute update equations in:
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2. SIGNAL PREDICTION STABILITY But we can also see that:
In which case we can express the derivative as:
And then V is bounded by a decaying exponential:
The system is semi-globally (due to constrained initial values) UUB
P must remain invertible (creates problems)
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2. SIGNAL PREDICTION (CONT’D)
Separated Signal
MLP
128-point Time
Domain Buffer
Predicted Signal
+
K
+
1/s
G
X
|| ||
+
tanh
F
A
+
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2. SIGNAL PREDICTION (CONT’D) Control system offers adjustment to improve prediction
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2. SIGNAL PREDICTION (CONT’D) Weight convergence (constant frequency sinusoids)
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3. CANCEL THE CONTAMINANTS Depending on the desired signal, only one of the predictions is used C1 and C2 are found during the separation stage
Predicted Signal
C1
Signal at Ear 1
+Audible Signal
at Ear 1
Predicted Signal
C2
Signal at Ear 2
+Audible Signal
at Ear 2
+
+
Signal at Ear 1
Signal at Ear 2
Separated Signal 2
Separated Signal 11/(1-C11C21)
Z-11/(1-C11C21) Z-1 Z-1 Z-1
Z-11/(1-C11C21) Z-1 Z-1 Z-1
-C11
C21
1/(1-C11C21)
C11
C22
+
C23 C24 C2n
-C12 -C13 -C14 -C1m
-C22 -C23 -C24 -C2n
C21 +
C12 C13 C14 C1m
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3. CANCEL THE CONTAMINANTS (CONT’D)
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SUMMARY FOR TEST SIGNALS
Sin
e
Sum
of
sines
Sm
oke A
larm
Man's
Voic
e
Gir
l's
Voic
e
Mach
inery
Nois
y S
ines
Dri
ll0
10
20
30
40
50
60
70
Crosstalk reduction in select signals
Sine
Sum of Sines
Smoke Alarm
Man's Voice
Girl's Voice
Machinery
Noisy Sine
Drill
Contaminating Signal
Redu
ction
in C
ross
talk
(dB)
DESIRED SIGNAL:
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REFERENCES [1] “Cocktail Party Effect,” Wikipedia. Available:
http://en.wikipedia.org/wiki/Cocktail_party_effect
[2] C. Jutten and J. Herault, “Blind Separation of Sources, Part I: An Adaptive Algorithm Based on Neuromimetic Architecture,” Signal Proc, vol. 21, no. 1, pp. 1-10, July 1991.
[3] G. W. Ng. Application of Neural Networks to Adaptive Control of Nonlinear Systems. Somerset: Research Studies Press, 1997, pp. 103-133.
[4] J. A. K. Suykens, J. P. L. Vandewalle, and B. L. R. De Moor. Artificial Neural Networks for Modelling and Control of Non-Linear Systems. Netherlands: Kluwer Academic Publishers, 1996, pp. 46-49.
[5] M. T. Pourazad et al, “Heart sound cancellation from lung sound recordings using time-frequency filtering,” Med Biol Eng Comput, no. 44, pp. 216-225, 2006.
[6] J. M. Diebele et al, “Dynamic Separation of pulmonary and cardiac changes in electrical impedance tomography,” Physiol Meas, vol. 29, no. 6, pp. 1-14, 2008.
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