Evaluation of separation and dereverberation performance in frequency domain blind source separation

In this paper, we propose a new method for evaluating the separation and dereverberation performance of a convolutive blind source separation (BSS) system, and investigate a separating system obtained by employing frequency domain BSS based on independent component analysis (ICA). As a result, we reveal the acoustical characteristics of the frequency domain BSS for convolutive mixture of speech signals. We show that the separating system removes the direct sound of a jammer signal even when the frame length is relatively short, and it also reduces the reverberation of the jammer according to the frame length. We also confirm that the reverberation of the target is not reduced. Moreover, we propose a technique, suggested by the experimental results, for improving the quality of the separated signals by removing pre-echo noise.

[1]  Dieter Filbert,et al.  SEMI-BLIND SOURCE SEPARATION FOR MACHINE MONITORING , 2001 .

[2]  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).

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

[4]  Futoshi Asano,et al.  EVALUATION AND REAL-TIME IMPLEMENTATION OF BLIND SOURCE SEPARATION SYSTEM USING TIME-DELAYED DECORRELATION , 2000 .

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

[6]  Dennis R. Morgan,et al.  Exploring permutation inconsistency in blind separation of speech signals in a reverberant environment , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[7]  Allan Kardec Barros,et al.  Real world blind separation of convolved non-stationary signals , 1999 .

[8]  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).

[9]  Jiangtao Xi,et al.  Blind separation and restoration of signals mixed in convolutive environment , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Kari Torkkola,et al.  Blind separation of convolved sources based on information maximization , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

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

[12]  K. Matsuoka,et al.  Minimal distortion principle for blind source separation , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[13]  Reinhold Orglmeister,et al.  Blind source separation of real world signals , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[14]  Paris Smaragdis,et al.  Evaluation of blind signal separation methods , 1999 .

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

[16]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

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

[18]  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.

[19]  Christopher V. Alvino,et al.  Geometric source separation: merging convolutive source separation with geometric beamforming , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[20]  William M. Hartmann,et al.  Psychoacoustics: Facts and Models , 2001 .

[21]  Hugo Fastl,et al.  Psychoacoustics: Facts and Models , 1990 .