Two pairwise iterative schemes for high dimensional blind source separation

We propose a frequency-domain method based on robust independent component analysis (RICA) to address the multichannel Blind Source Separation (BSS) problem of convolutive speech mixtures in highly reverberant environments. We impose regularization processes to tackle the ill-conditioning problem of the covariance matrix and to mitigate the performance degradation in the frequency domain. We apply an algorithm to separate the source signals in adverse conditions, i.e. high reverberation conditions when short observation signals are available. Furthermore, we study the impact of several parameters on the performance of separation, e.g. overlapping ratio and window type of the frequency domain method. We also compare different techniques to solve the frequency-domain permutation ambiguity. Through simulations and real-world experiments, we verify the superiority of the presented convolutive algorithm among other BSS algorithms, including recursive regularized ICA (RR-ICA), independent vector analysis (IVA).

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Two pairwise iterative schemes for high dimensional blind source separation

Cited by 3 publications

References 27 publications

Channel estimation for massive MIMO system using the shannon entropy function

Channel estimation for massive MIMO system using the shannon entropy function

English speech sound improvement system based on deep learning from signal processing to semantic recognition

A RobustICA-based algorithmic system for blind separation of convolutive mixtures

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