Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Formulation of Independent Vector Analysis Using Complex Gaussian Scale Mixtures
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Independent vector analysis incorporating active and inactive states
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Separating underdetermined convolutive speech mixtures
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Multivariate scale mixture of gaussians modeling
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Performance analysis of minimum ℓ1-norm solutions for underdetermined source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Blind Source Separation Exploiting Higher-Order Frequency Dependencies
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
Blind Extraction of Dominant Target Sources Using ICA and Time-Frequency Masking
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Neural Networks
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Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that significantly reduces the occurrence of the well-known permutation problem in frequency domain blind source separation (BSS). In this paper, we develop a novel IVA-based unifying framework for overcomplete/complete/ undercomplete convolutive noisy BSS. We show that in order for the sources to be separable in the frequency domain, they must have a temporal dynamic structure. We exploit a common form of dynamics, especially present in speech, wherein the signals have silence periods intermittently, hence varying the set of active sources with time. This feature is extremely useful in dealing with overcomplete situations. An approach using hidden Markov models (HMMs) is proposed that takes advantage of different combinations of silence gaps of the source signals at each time period. This enables the algorithm to "glimpse" or listen in the gaps, hence compensating for the global degeneracy by allowing it to learn the mixing matrices at periods where it is locally less degenerate. The same glimpsing strategy can be employed to the complete/under-complete case as well. Moreover, additive noise is considered in our model. Real and simulated experiments were carried out for overcomplete convoluted mixtures of speech signals yielding improved separation results compared to a sparsity-based robust time-frequency masking method. Signal-to-disturbance ratio (SDR) and machine intelligibility of a speech recognizer was used to evaluate their performances. Experiments were also conducted for the classical complete setting using the proposed algorithm and compared with standard IVA showing that the results compare favorably.