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Blind Separation Algorithm for Audio Signal Based on Genetic Algorithm and Neural Network

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Blind Separation Algorithm for Audio Signal Based on Genetic Algorithm and Neural Network. 2008 International Symposium on Information Science and Engineering. Dahui Li , Ming Diao and Xuefeng Dai. Presenter: Jain_De ,Lee. OUTLINE. INTRODUCTION PROBLEM DESCRIPTION ALGORITHM DESCRIPTION

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Blind Separation Algorithm for Audio Signal Based on Genetic Algorithm and Neural Network

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  1. Blind Separation Algorithm for Audio SignalBased on Genetic Algorithm and Neural Network 2008 International Symposium on Information Science and Engineering Dahui Li , Ming Diao and Xuefeng Dai Presenter: Jain_De,Lee

  2. OUTLINE • INTRODUCTION • PROBLEM DESCRIPTION • ALGORITHM DESCRIPTION • SIMULATION EXPERIMENT • CONCLUSION

  3. INTRODUCTION • The 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 Complicated computation

  4. INTRODUCTION • ICA 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

  5. PROBLEM DESCRIPTION • Composite Separation Model S(t): source signal vector X=AS X(t): observation signal vector [Wij]n×n [aij]n×n : transmission matrix Y(t): signal vector of the separation outputs

  6. ALGORITHM DESCRIPTION Genetic Algorithm output signal

  7. GENETIC ALGORITHM DESCRIPTION • Genetic Algorithm Operation • Reproduction / Selection • Crossover • Mutation • Reproduction / Selection • roulette wheel selection • tournament selection 22.7% 5.6% 23.6% 42.3% 5.8%

  8. GENETIC ALGORITHM DESCRIPTION • Crossover • 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 0 0 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 1 0 1 Mask

  9. ALGORITHM DESCRIPTION • Pretreatment • 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

  10. ALGORITHM DESCRIPTION • Makes 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 :

  11. SIMULATION EXPERIMENT • Experimental Condition • Data Sampling Frequency – 10 kHz • Audio Signal • Transmission Matrix Agriculture car signal Truck signal

  12. SIMULATION EXPERIMENT • Mixed Signal Agriculture car mixture signal Truck mixture signal

  13. SIMULATION EXPERIMENT The Convergence Speed of the Two Algorithms

  14. SIMULATION EXPERIMENT • The signal separation matrix w • Separate signals • Joint moment Agriculture car separation signal Truck separation signal E(A,W-1)=0.0854

  15. CONCLUSION • The 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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