Blind Signal Separation of Strong Reverberation Based on a New Algorithm

Hu Xiong Li; Fan Gu

doi:10.4028/www.scientific.net/AMM.63-64.395

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 63-64Blind Signal Separation of Strong Reverberation...

Blind Signal Separation of Strong Reverberation Based on a New Algorithm

Abstract:

The major problem of blind source separation in frequency domain is the permutation ambiguity between different frequency bins, which is the key factor to recover the original sources correctly. A new idea is to consider the frequency components from the same source as a multivariate vector with a certain probability density function, and the vectors from different sources are independent each other. An algorithm based on this idea is proposed to solve the permutation ambiguity problem of BSS in frequency domain, and some approximate cost functions are compared with the existing algorithm in frequency domain. The computer simulations to two true speeches with strong reverberation are shown to verify the efficiency of the proposed algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 63-64)

Pages:

395-398

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.63-64.395

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Online since:

June 2011

Authors:

Hu Xiong Li, Fan Gu

Keywords:

Blind Source Separation (BSS), Multivariate PDF, Strong Reverberant Environment

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References

[1] A. Hyvarinen, et al. Independent component analysis. John Wiley and Sons, (2001).

Google Scholar

[2] J. Benesty, M. Mohan Sonhi, Yiteng Huang, et al. Handbook of Speech Processing, Springer (2008).

Google Scholar

[3] H. Saruwatari, S. Kurita, K. takeda, Blind source separation combining frequency-domain ICA and beamforming, ICASSP 2001, 5(7), 2733-2736.

DOI: 10.1109/icassp.2001.940211

Google Scholar

[4] N. Murata, S. Ikeda, A. Ziehe, An approach to blind source separation based on temporal structure of speech signals, Neurocomputing, 2001, 41(1-4), 1-24.

DOI: 10.1016/s0925-2312(00)00345-3

Google Scholar

[5] Zhu Jianjian, Wang Huigang, Li Huxiong, Joint algorithm for permutation problem in frequency-domain in blind speech source separation, Computer Applications, 2008, 28(6), 1552-1554. (in chinese).

DOI: 10.3724/sp.j.1087.2008.01552

Google Scholar

[6] Intae Lee, Taesu Kim, Te-won Lee, Fast fixed-point independent vector analysis algorithms for convolutive blind source separation, Signal Processing, vol. 87 , 2007, 1859-1871.

DOI: 10.1016/j.sigpro.2007.01.010

Google Scholar

[7] Matsuoka K., Nakashima S. Minimal distortion principle for blind source separation. Proceeding of the 41st SICE Annual (SICE 2002), Washington, 2002, 4, 2138-2143.

DOI: 10.1109/sice.2002.1195729

Google Scholar

[8] M. Davies, Audio Source Separation, Mathematics in Signal Processing 5, Oxford University Press, p.57–68, (2002).

Google Scholar