Bin-Wise Combination of Time-Frequency Masking and Beamforming for Convolutive Source Separation

This paper presents a new Blind Source Separation (BSS) method for convolutive mixtures that can be underdeter-mined. Exploiting the sparsity of the source signals in the Time-Frequency (TF) domain, this method combines TF masking and beamforming. Indeed, on the one hand, BSS methods based on TF masking achieve remarkable performance even in the underdeter-mined case, however they tend to cause artifacts at the separated sources. On the other hand, beamforming can achieve good performance in the (over)-determined case without distorting the estimated signals. Therefore, combining these two techniques makes it possible to benefit from both their advantages. In the proposed method, unlike existing methods that use beamforming with TF masking, we introduce new normalized directional vectors to generate the different beamformers involved, and a new way for better estimating these vectors. In addition, we propose a new technique that can be used to separate sources in the case of underdetermined mixtures. Test results showed good performance for our method compared to various existing methods, similar in terms of working hypotheses, both in the determined and underdetermined cases.