Abstract
Blind source separation (BSS) for convolutive mixtures can be performed efficiently in the frequency domain, where independent component analysis (ICA) is applied separately in each frequency bin. To solve the permutation problem of frequency-domain BSS robustly, information regarding the number of sources is very important. This paper presents a method for estimating the number of sources from convolutive mixtures of sources. The new method estimates the power of each source or noise component by using ICA and a scaling technique to distinguish sources and noises. Also, a reverberant component can be identified by calculating the correlation of component envelopes. Experimental results for up to three sources show that the proposed method worked well in a reverberant condition whose reverberation time was 200 ms.
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References
Haykin, S. (ed.): Unsupervised adaptive filtering. Blind source separation, vol. I. John Wiley & Sons, Chichester (2000)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis. John Wiley & Sons, Chichester (2001)
Rickard, S., Balan, R., Rosca, J.: Blind source separation based on space-timefrequency diversity. In: Proc. ICA 2003, pp. 493–498 (2003)
Matsuoka, K., Nakashima, S.: Minimal distortion principle for blind source separation. In: Proc. ICA 2001, pp. 722–727 (2001)
Douglas, S.C., Sun, X.: Convolutive blind separation of speech mixtures using the natural gradient. Speech Communication 39, 65–78 (2003)
Smaragdis, P.: Blind separation of convolved mixtures in the frequency domain. Neurocomputing 22, 21–34 (1998)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41, 1–24 (2001)
Sawada, H., Mukai, R., Araki, S., Makino, S.: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Trans. Speech and Audio Processing 12 (2004)
Mukai, R., Sawada, H., de la Kethulle, S., Araki, S., Makino, S.: Array geometry arrangement for frequency domain blind source separation. In: Proc. IWAENC 2003, pp. 219–222 (2003)
Sawada, H., Mukai, R., Araki, S., Makino, S.: Convolutive blind source separation for more than two sources in the frequency domain. In: Proc. ICASSP 2004 (2004)
Wax, M., Kailath, T.: Detection of signals by information theoretic criteria. IEEE Trans. Acoustics, Speech, and Signal Processing 33, 387–392 (1985)
Yamamoto, K., Asano, F., van Rooijen, W., Ling, E., Yamada, T., Kitawaki, N.: Estimation of the number of sound sources using support vector machines and its application to sound source separation. In: Proc. ICASSP 2003, pp. 485–488 (2003)
Winter, S., Sawada, H., Makino, S.: Geometrical interpretation of the PCA subspace method for overdetermined blind source separation. In: Proc. ICA 2003, pp. 775–780 (2003)
Sawada, H., Mukai, R., de la Kethulle, S., Araki, S., Makino, S.: Spectral smoothing for frequency-domain blind source separation. In: Proc. IWAENC 2003, pp. 311–314 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Sawada, H., Winter, S., Mukai, R., Araki, S., Makino, S. (2004). Estimating the Number of Sources for Frequency-Domain Blind Source Separation. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_78
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DOI: https://doi.org/10.1007/978-3-540-30110-3_78
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