A multistage approach to blind separation of convolutive speech mixtures

We propose a novel algorithm for the separation of convolutive speech mixtures using two-microphone recordings, based on the combination of independent component analysis (ICA) and ideal binary mask (IBM), together with a post-filtering process in the cepstral domain. The proposed algorithm consists of three steps. First, a constrained convolutive ICA algorithm is applied to separate the source signals from two-microphone recordings. In the second step, we estimate the IBM by comparing the energy of corresponding time–frequency (T–F) units from the separated sources obtained with the convolutive ICA algorithm. The last step is to reduce musical noise caused by T–F masking using cepstral smoothing. The performance of the proposed approach is evaluated using both reverberant mixtures generated using a simulated room model and real recordings in terms of signal to noise ratio measurement. The proposed algorithm offers considerably higher efficiency and improved speech quality while producing similar separation performance compared with a recent approach.

Research highlights
► A multistage algorithm is developed to separate convolutive speech mixtures.
► Binary masking improves the speech separation performance by ICA approach.
► Cepstral smoothing reduces musical noise.