A fast fixed-point algorithm for independent component analysis
Neural Computation
Solving the permutation problem in convolutive blind source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Equalization for discrete multitone transceivers to maximize bitrate
IEEE Transactions on Signal Processing
A method for filter equalization in convolutive blind source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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An often used approach for separating convolutive mixtures is the transformation to the time-frequency domain where an instantaneous ICA algorithm can be applied for each frequency separately. This approach leads to the so called permutation and scaling ambiguity. While different methods for the permutation problem have been widely studied, the solution for the scaling problem is usually based on the minimal distortion principle. We propose an alternative approach that shapes the unmixing filters to have an exponential decay which mimics the form of room impulse responses. These new filters still add some reverberation to the restored signals, but the audible distortions are clearly reduced. Additionally the length of the unmixing filters is reduced, so these filters will suffer less from circular-convolution effects that are inherent to unmixing approaches based on bin-wise ICA followed by permutation and scaling correction. The results for the new algorithm will be shown on a real-world example.