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

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