Equivalence between frequency-domain blind source separation and frequency-domain adaptive beamforming for convolutive mixtures

Authors:
Shoko Araki;Shoji Makino;Yoichi Hinamoto;Ryo Mukai;Tsuyoki Nishikawa;Hiroshi Saruwatari
Affiliations:
NTT Communication Science Laboratories, NTT Corporation, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan;NTT Communication Science Laboratories, NTT Corporation, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan;NTT Communication Science Laboratories, NTT Corporation, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2003

Citing 7
Cited 7

An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Independent component analysis: theory and applications

Independent component analysis: theory and applications
Exploring permutation inconsistency in blind separation of speech signals in a reverberant environment

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Evaluation of blind signal separation method using directivity pattern under reverberant conditions

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Blind source separation combining frequency-domain ICA and beamforming

ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech

ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Signal separation by symmetric adaptive decorrelation: stability,convergence, and uniqueness

IEEE Transactions on Signal Processing

Frequency-domain blind source separation of many speech signals using near-field and far-field models

EURASIP Journal on Applied Signal Processing
Robust Source Separation with Simple One-Source-Active Detection

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Beamforming initialization and data prewhitening in natural gradient convolutive blind source separation of speech mixtures

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Spatial efficiency of blind source separation based on decorrelation - subjective and objective assessment

Speech Communication
A Geometrically Constrained ICA Algorithm for Blind Separation in Convolutive Environments

Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Independent component analysis and time-frequency masking for speech recognition in multitalker conditions

EURASIP Journal on Audio, Speech, and Music Processing
Modulation domain blind speech separation in noisy environments

Speech Communication

Quantified Score

Hi-index	0.02

Visualization

Abstract

Frequency-domain blind source separation (BSS) is shown to be equivalent to two sets of frequency-domain adaptive beamformers (ABFs) under certain conditions. The zero search of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABFs. The unmixing matrix of the BSS and the filter coefficients of the ABFs converge to the same solution if the two source signals are ideally independent. If they are dependent, this results in a bias for the correct unmixing filter coefficients. Therefore, the performance of the BSS is limited to that of the ABF if the ABF can use exact geometric information. This understanding gives an interpretation of BSS from a physical point of view.