Non Linear Blind Source Separation Using Cross Power Spectral Density and Cross Correlation Coefficient
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Abstract
Blind source separation (BSS) is an emerging technique, which enables the extraction of target speech from observed mixed speeches. BSS algorithm sare based on restoring the statistical independence of the source signals. This paper is concerned with blind source separation of convolutive mixtures of acoustic signals, especially speech in which the source signals are instantaneously mixed in unknown nonlinear processes. A statistical an d computational technique, called independent component analysis (ICA), used for BSS, is examined by achieving maxim um entropy at the output end, also cross power spectral density and
cross correlation coefficient o f observed unmixed signal an d source signal is check to conform the received signal is the original signal. Effectiveness o f the proposed method is validated in BSS of male and female voice signals gets signal to interference ratio
8.75 with the proposed, algorithm , 7.03 an d 5.12 with algorithm based on Mutual Information and non causal FIR filter. This paper synthesizes proposed algorithm on Field Program m able Gate Array (FPGA) - Xilinx VIRTEXV1OOOE that executes a t the maximum frequency o f 12.288 MHz. The performance com parisons between the proposed an d another two ICA-related FPGA implementations [4][5], show that the proposed FPGA implementation has potential in performing complicated algorithms on large volume datasets.
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References
1. Charayaphan Charoensak, Members IEEE and Farook Sattar, Members IEEE, “System- Level Design o f Low Cost FPGA Hardware for Real-Tim e ICA-Based Blind Source Separation”, IEEE Transaction on signal processing, 2004 pp. 139-140.
2. Charayaphan Charoensak, MIEEE and Farook Sattar, MIEEE, “A Single- Chip FPGA Design fo r Real-Time ICA-
B ased Blind Source Separation Algorithm”, IEEETransactionon signal processing, 2005 IEEE. Pg.5822- 5825. j
3. Dinh-Tuan Pham, “Fast Algorithms fo r Mutual Inform ation B ased Independent C om ponent Analysis”, IEEE Transaction on signal processing, Vol. 52, No. 10, October 2004.
4. H.P.D. Xia, S.C. Douglas, and K. F. Smith, “A Scalable VLSI Architecture fo r M ultichannel Blind D econvolution and Source Separation”, 1998, pp. 297-306.
5. Hyoung-Gook, Nicolas Moreau and Thomas Sikora, “Audio Classification based on MPEG-7 Spectral Representation”, IEEE transaction on circuits and systems for video technology, Vol. 14, NO. 5 May 2004
6. Hyung-Min Park, Sang-Hoon Oh and Soo-Young Lee, “Adaptive Noise Canceling Based on Independent Component Analysis”, Electronics letters, Vol. 38 No. 15,18 July 2002.
7. Janusz Starzyk and Yongtao Guo, “An Entropy-based Learning Hardware Organization Using FPGA”, IEEE Transactions on signal processing 2001.
8. Jordi Sole-Casalsa, and Marcos Faundez-Zanuyb, “Application o f the MutualInformtion Minimization to Speaker Recognition! Identification Improvement ”, Neurocomputing Dec 2005.
9. K. Nakayama, A. Hirano and A. Horita, “A Learning Algorithm with A daptive Exponential Stepsize fo r Blind Source
Separation o f ConvolutiveM ixtures with Reverberations”, IEEE INNS, Proc. UGNN'2003 July 2003.
10. K. Nakayama, A. Hirano andY. Dejima, “Analysis o f Signal Separation an d Distortion in Feedforw ard Blind Source Separationfor Convolutive Mixture”, Proc. MWSCAS2004, Hiroshima, Japan, pp. 111-207-111-210, July 2004.
11. LEE.T.-W. “Independent C om ponent Analysis”, Kluwer Academic Publishers, Boston, 1998
12. Shoko Arukit, Slioji Mukino, Robert Aichner, Tsuyoki Nashikawa, Hiroshi Saruwutari, “Subband B ased Blind Source Separation fo rConvolutive Mixtures ofS peech”, IEEE 2003, pp.509-512.
13. Vicente Zarzoso and Asoke K. Nandi, “A daptive Blind Source Separation fo r Virtually any Source Probability Density Function”, IEEE Transactions on signal processing, vol. 48, no. 2, February 2000.