2008 International Symposium on Information Science and Engineering

Presenter: Jain_De ,Lee

Dahui Li , Ming Diao and Xuefeng Dai

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OUTLINEINTRODUCTION

PROBLEM DESCRIPTION

ALGORITHM DESCRIPTION

SIMULATION EXPERIMENT

CONCLUSION

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INTRODUCTIONThe Core of Blind Separation Problem

† Getting separation matrix

Error Backpropagation Algorithm† Fall into Local optimal trap

ICA Based on Information Theory† Have better separation† Only appropriate for non-Gauss† Complicated computation and convergence

slowly

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INTRODUCTIONICA Based on Measurement of Non-Gaussian

† Has the quickly calculation† Good statistical characteristics and robustness† Separation result often inaccurate

Neural Network Algorithm and the Genetic Algorithm

† Have less restrictions on optimization problems† Not be continuous or differentiable

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PROBLEM DESCRIPTION

S(t): source signal vectorX(t): observation signal vector[aij]n×n : transmission matrix

Y(t): signal vector of the separation outputs

Composite Separation Model

X=AS[Wij]n×n

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ALGORITHM DESCRIPTION

Gen

etic

Alg

orith

m

output signal

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GENETIC ALGORITHM DESCRIPTIONGenetic Algorithm Operation

† Reproduction / Selection† Crossover† Mutation

Reproduction / Selection† roulette wheel selection† tournament selection

42.3%

22.7%

23.6%

5.6%

5.8%

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GENETIC ALGORITHM DESCRIPTIONCrossover

† Setting crossover probability(0.8~1)† Crossover types

‡ 1-point crossover‡ 2-point crossover‡ Mask crossover

Mutation† Setting mutation probability(0.01~0.08)

0 1

1

0 01 1 1

1 0 0 011

0 1

1

0 01 1 1

1 0 0 011

0 1

1

0 01 1 1

1 0 0 011

Mask 1 1 10000

10 1 1 00 10

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ALGORITHM DESCRIPTIONPretreatment

† Centering– m=E{x} 、 E{x-m}=0† Whitening –use of PCA(Principal Component

Analysis )

Generates Initial Separation Matrixes† Randomly generate 50 separation matrixes† Consist of chromosome of 8 bit binary code

Calculates y=wx

E{xxT}=EDET 、 z=Vx=ED-1/2ETx

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ALGORITHM DESCRIPTIONMakes y Centering and Whitening

Calculates the fitness values

Determine the signal whether Correct† TRUE– Output signal and end the process† FALSE– Take the crossover or mutation operation

Fitness function :

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SIMULATION EXPERIMENTExperimental Condition

† Data Sampling Frequency – 10 kHz† Audio Signal

† Transmission Matrix

Truck signalAgriculture car signal

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SIMULATION EXPERIMENTMixed Signal

Truck mixture signal

Agriculture car mixture signal

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SIMULATION EXPERIMENT

Error Backpropagation

Genetic Neural Network

Convergence

9000 5000

Convergence Speed

SLOW FAST

Degree of Convergen

ce not accuracy accuracy

The Convergence Speed of the Two Algorithms

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SIMULATION EXPERIMENTThe signal separation matrix w

Separate signals

Joint momentE(A,W-1)=0.0854

Truck separation signalAgriculture car separation signal

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CONCLUSIONThe algorithm has the characteristics of

convergence quickly and separation effectively

cross-operation and mutation operation lead to chain issues

Future research topic† The source signals number is less than that of

observation signals† Non-Gaussian noise† Pulsing signal

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