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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References
Hyvarinen, A., et al.: Independent component analysis. John Wiley and Sons, Chichester (2001)
Benesty, J., Sonhi, M.M., Huang, Y., et al.: Handbook of Speech Processing. Springer, Heidelberg (2008)
Saruwatari, H., Kurita, S., Takeda, K.: Blind source separation combining frequency-domain ICA and beamforming. ICASSP 5(7), 2733–2736 (2001)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41(1-4), 1–24 (2001)
Zhu, J., Wang, H., Li, H.: Joint algorithm for permutation problem in frequency-domain in blind speech source separation. Computer Applications 28(6), 1552–1554 (2008) (in chinese)
Lee, I., Kim, T., Lee, T.-w.: Fast fixed-point independent vector analysis algorithms for convolutive blind source separation. Signal Processing 87, 1859–1871 (2007)
Matsuoka, K., Nakashima, S.: Minimal distortion principle for blind source separation. In: Proceeding of the 41st SICE Annual (SICE 2002), Washington, vol. 4, pp. 2138–2143 (2002)
Davies, M.: Audio Source Separation. Mathematics in Signal Processing, vol. 5, pp. 57–68. Oxford University Press, Oxford (2002)
Lee, I., Lee, T.-W.: On the Assumption of Spherical Symmetry and Sparseness for the Frequency-Domain Speech Model. IEEE Trans. on Speech, Audio and Language Processing 15(5), 1521–1528 (2007)
Lehmann, E., Johansson, A.: Diffuse Reverberation Model for Efficient Image-Source Simulation of Room Impulse Responses. IEEE Trans. on Audio, Speech and Language Processing 18(6), 1429–1439 (2010)
Hiroe, A.: Solution of permutation problem in frequency domain ICA, using multivariate probability density functions. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 601–608. Springer, Heidelberg (2006)
Araki, S., Mukai, R., Makino, S., Nishikawa, T., et al.: The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech. IEEE Trans. Speech and Audio Processing 11, 109–116 (2003)
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Li, H., Fan, G. (2011). A New Improved Method to Permutation Ambiguity in BSS with Strong Reverberation. In: Shen, G., Huang, X. (eds) Advanced Research on Electronic Commerce, Web Application, and Communication. ECWAC 2011. Communications in Computer and Information Science, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20367-1_34
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DOI: https://doi.org/10.1007/978-3-642-20367-1_34
Publisher Name: Springer, Berlin, Heidelberg
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