A fast fixed-point algorithm for independent component analysis
Neural Computation
A Method for Filter Shaping in Convolutive Blind Source Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Using the scaling ambiguity for filter shortening in convolutive blind source separation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
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The separation of convolutive mixed signals can be carried out in the time-frequency domain, where the task is reduced to multiple instantaneous problems. This direct approach leads to the permutation and scaling problems, but it is possible to introduce an objective function in the time-frequency domain and minimize it with respect to the time domain coefficients. While this approach allows for the elimination of the permutation problem, the unmixing filters can be quite distorted due the unsolved scaling problem. In this paper we propose a method for equalization of these filters by using the scaling ambiguity. The resulting filters have a characteristic of a Dirac pulse and introduce less distortion to the separated signals. The results are shown on a real-world example.