Permutation correction in the frequency domain in blind separation of speech mixtures
EURASIP Journal on Applied Signal Processing
A comparison of algorithms for fitting the PARAFAC model
Computational Statistics & Data Analysis
Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures
IEEE Transactions on Audio, Speech, and Language Processing
Blind PARAFAC receivers for DS-CDMA systems
IEEE Transactions on Signal Processing
Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization
IEEE Transactions on Signal Processing
Parallel factor analysis in sensor array processing
IEEE Transactions on Signal Processing
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This paper considers separation of convolutive speech mixtures in frequency-domain within a tensorial framework. By assuming that components associated with neighboring frequency bins of the same source are still correlated, a set of cross-frequency covariance tensors with trilinear structure are established, and an algorithm consisting of consecutive parallel factor (PARAFAC) decompositions is developed. Each PARAFAC decompositon used in the proposed method can simultaneously estimate two neighboring frequency responses, one of which is a common factor with the subsequent crossfrequency covariance tensor, and thus could be used to align the permutations of the estimates in all the PARAFAC decompositions. In addition, the issue of identifiability is addressed, and simulations with synthetic speech signals are provided to verify the efficacy of the proposed method.