Journal of VLSI Signal Processing Systems
EURASIP Journal on Applied Signal Processing
A Speech Enhancement Method in Subband
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Robust Source Separation with Simple One-Source-Active Detection
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Fast Convergence Blind Source Separation Using Frequency Subband Interpolation by Null Beamforming
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Signal separation by integrating adaptive beamforming with blind deconvolution
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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
Correlation-based amplitude estimation of coincident partials in monaural musical signals
EURASIP Journal on Audio, Speech, and Music Processing
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on environmental sound synthesis, processing, and retrieval
Computers and Electrical Engineering
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We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the slow-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (a) frequency-domain ICA with direction-of-arrival (DOA) estimation, (b) beamforming based on the estimated DOA, and (c) integration of (a) and (b) based on the algorithm diversity in both iteration and frequency domain. The unmixing matrix obtained by ICA is temporally substituted by the matrix based on beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.