Convolutive blind separation of speech mixtures using the natural gradient
Speech Communication - Special issue on speech processing for hearing aids
Blind source separation combining independent component analysis and beamforming
EURASIP Journal on Applied Signal Processing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind vector deconvolution: convolutive mixture models in short-time fourier transform domain
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
IEEE Transactions on Audio, Speech, and Language Processing
Blind source separation based on a fast-convergence algorithm combining ICA and beamforming
IEEE Transactions on Audio, Speech, and Language Processing
Spatio–Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures
IEEE Transactions on Audio, Speech, and Language Processing
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Frequency-domain blind source separation (BSS) performs poorly in high reverberation because the independence assumption collapses at each frequency bins when the number of bins increases. To improve the separation result, this paper proposes a method which combines two techniques by using beamforming as a preprocessor of blind source separation. With the sound source locations supposed to be known, the mixed signals are dereverberated and enhanced by beamforming; then the beamformed signals are further separated by blind source separation. To implement the proposed method, a superdirective fixed beamformer is designed for beamforming, and an interfrequency dependence-based permutation alignment scheme is presented for frequency-domain blind source separation. With beamforming shortening mixing filters and reducing noise before blind source separation, the combined method works better in reverberation. The performance of the proposed method is investigated by separating up to 4 sources in different environments with reverberation time from 100 ms to 700 ms. Simulation results verify the outperformance of the proposed method over using beamforming or blind source separation alone. Analysis demonstrates that the proposed method is computationally efficient and appropriate for real-time processing.