Reverberant Audio Blind Source Separation via Local Convolutive Independent Vector Analysis

In this paper, we propose a new formulation for the blind source separation problem for audio signals with convolutive mixtures to improve the separation performance of Independent Vector Analysis (IVA). The proposed method benefits from both the recently investigated convolutive approximation model and the IVA approaches that take advantage of the cross-band information to avoid permutation alignment. We first exploit the link between the IVA and the Sparse Component Analysis (SCA) methods through the structured sparsity. We then propose a new framework by combining the convolutive narrowband approximation and the Windowed- Group-Lasso (WGL). The optimization of the model is based on the alternating optimization approach where the convolutive kernel and the source components are jointly optimized. The proposed approach outperforms the existing methods through numerical evaluations in terms of objective measures.