Cumulative State Coherence Transform for a Robust Two-Channel Multiple Source Localization
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Estimating Phase Linearity in the Frequency-Domain ICA Demixing Matrix
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Under-determined reverberant audio source separation using a full-rank spatial covariance model
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Blind source separation based on time-frequency sparseness in the presence of spatial aliasing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
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This paper proposes a new formulation and optimization procedure for grouping frequency components in frequency-domain blind source separation (BSS). We adopt two separation techniques, independent component analysis (ICA) and time-frequency (T-F) masking, for the frequency-domain BSS. With ICA, grouping the frequency components corresponds to aligning the permutation ambiguity of the ICA solution in each frequency bin. With T-F masking, grouping the frequency components corresponds to classifying sensor observations in the time-frequency domain for individual sources. The grouping procedure is based on estimating anechoic propagation model parameters by analyzing ICA results or sensor observations. More specifically, the time delays of arrival and attenuations from a source to all sensors are estimated for each source. The focus of this paper includes the applicability of the proposed procedure for a situation with wide sensor spacing where spatial aliasing may occur. Experimental results show that the proposed procedure effectively separates two or three sources with several sensor configurations in a real room, as long as the room reverberation is moderately low.