A neural net for blind separation of nonstationary signals
Neural Networks
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
On-line Convolutive Blind Source Separation of Non-Stationary Signals
Journal of VLSI Signal Processing Systems
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
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures
IEEE Transactions on Audio, Speech, and Language Processing
Speech separation via parallel factor analysis of cross-frequency covariance tensor
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Sensitivity of joint approximate diagonalization in FD BSS
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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This paper presents a method for blind separation of convolutive mixtures of speech signals, based on the joint diagonalization of the time varying spectral matrices of the observation records. The main and still largely open problem in a frequency domain approach is permutation ambiguity. In an earlier paper of the authors, the continuity of the frequency response of the unmixing filters is exploited, but it leaves some frequency permutation jumps. This paper therefore proposes a new method based on two assumptions. The frequency continuity of the unmixing filters is still used in the initialization of the diagonalization algorithm. Then, the paper introduces a new method based on the time-frequency representations of the sources. They are assumed to vary smoothly with frequency. This hypothesis of the continuity of the time variation of the source energy is exploited on a sliding frequency bandwidth. It allows us to detect the remaining frequency permutation jumps. The method is compared with other approaches and results on real world recordings demonstrate superior performances of the proposed algorithm.