Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Convolutive blind separation of speech mixtures using the natural gradient
Speech Communication - Special issue on speech processing for hearing aids
A probabilistic approach for blind source separation of underdetermined convolutive mixtures
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
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 separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Joint acoustic and modulation frequency
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Comparing measures of sparsity
IEEE Transactions on Information Theory
Model-based expectation-maximization source separation and localization
IEEE Transactions on Audio, Speech, and Language Processing
A frequency domain blind signal separation method based ondecorrelation
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Single-Mixture Audio Source Separation by Subspace Decomposition of Hilbert Spectrum
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models
IEEE Transactions on Audio, Speech, and Language Processing
Hidden Markov models for wavelet-based blind source separation
IEEE Transactions on Image Processing
Real and imaginary modulation spectral subtraction for speech enhancement
Speech Communication
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We propose a noise robust blind speech separation (BSS) method by using two microphones. We perform BSS in the modulation domain to take advantage of the improved signal sparsity and reduced musical tone noise in this domain over the conventional acoustic frequency domain processing. We first use modulation domain real and imaginary spectral subtraction (MRISS) to enhance both magnitude and phase spectra of the noisy speech mixture inputs. We then estimate the direction of arrivals (DOAs) of the speech sources from subband inter-sensor phase differences (IPDs) by using an asymmetric Laplacian mixture model (ALMM), cluster the full-band IPDs via the estimated DOAs, and perform time-frequency masking to separate the source signals, all in the modulation domain. Experimental evaluations in five types of noises have shown that the performance of the proposed method is robust in 0-10dB SNRs and it is superior to acoustic domain separation without MRISS.