Blind source separation with different sensor spacing and filter length for each frequency range

This paper presents a method for blind source separation using several separating subsystems whose sensor spacing and filter length can be configured individually. Each subsystem is responsible for source separation of an allocated frequency range. With this mechanism, we can use appropriate sensor spacing as well as filter length for each frequency range. We obtained better separation performance than with the conventional method by using a wide sensor spacing and a long filter for a low frequency range, and a narrow sensor spacing and a short filter for a high frequency range.

[1]  Lucas C. Parra,et al.  Convolutive blind separation of non-stationary sources , 2000, IEEE Trans. Speech Audio Process..

[2]  Hiroshi Sawada,et al.  Polar coordinate based nonlinear function for frequency-domain blind source separation , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[4]  Shoko Araki,et al.  Separation and dereverberation performance of frequency domain blind source separation for speech in a reverberant environment , 2001, INTERSPEECH.

[5]  E. Oja,et al.  Independent Component Analysis , 2013 .

[6]  Satoshi Nakamura,et al.  An evaluation of sound source identification with RWCP sound scene database in real acoustic environments , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[7]  Shoko Araki,et al.  The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech , 2003, IEEE Trans. Speech Audio Process..

[8]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[9]  Kazuya Takeda,et al.  Evaluation of blind signal separation method using directivity pattern under reverberant conditions , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[10]  Paris Smaragdis,et al.  Blind separation of convolved mixtures in the frequency domain , 1998, Neurocomputing.

[11]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[12]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[13]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[14]  Shoko Araki,et al.  Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[15]  C. Burrus,et al.  Array Signal Processing , 1989 .

[16]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[17]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[18]  Shiro Ikeda,et al.  A METHOD OF ICA IN TIME-FREQUENCY DOMAIN , 2003 .

[19]  Shoko Araki,et al.  Equivalence between frequency domain blind source separation and frequency domain adaptive null beamformers , 2001, INTERSPEECH.