Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources

Author:

KAMEOKA Hirokazu12,SATO Misa3,ONO Takuma1,ONO Nobutaka4,SAGAYAMA Shigeki1

Affiliation:

1. Graduate School of Information Science and Technology, The University of Tokyo

2. NTT Communication Science Laboratories, NTT Corporation

3. School of Engineering, The University of Tokyo

4. National Institute of Informatics

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Reference18 articles.

1. [1] A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.

2. [2] Ö. Yilmaz and S. Rickard, “Blind separation of speech mixtures via time-frequency masking,” IEEE Trans. Signal Process., vol.52, no.7, pp.1830-1847, 2004.

3. [3] M.I. Mandel, D.P.W. Ellis, and T. Jebara, “An EM algorithm for localizing multiple sound sources in reverberant environments,” Adv. Neural Info. Process. Syst., pp.953-960, 2006.

4. [4] S. Araki, H. Sawada, R. Mukai, and S. Makino, “Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors,” Signal Process., vol.87, no.8, pp.1833-1847, 2007.

5. [5] Y. Mori, H. Saruwatari, T. Takatani, S. Ukai, K. Shikano, T. Hiekata, and T. Morita, “Real-time implementation of two-stage blind source separation combining SIMO-ICA and binary masking,” Proc. 9th International Workshop on Acoustic Echo and Noise Control (IWAENC 2005), pp.229-232, 2005.

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