A RobustICA Based Algorithm for Blind Separation of Convolutive Mixtures
This work addresses the challenge of separating speech signals in adverse acoustic conditions, which is incremental as it builds on existing robust ICA methods with specific enhancements for convolutive mixtures.
The paper tackles the problem of blind source separation for convolutive speech mixtures in highly reverberant environments by proposing a frequency domain method based on robust independent component analysis, achieving superior performance compared to other algorithms like recursive regularized ICA and independent vector analysis through simulations and real-world experiments.
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).