Audio-visual convolutive blind source separation

We present a novel method for speech separation from their audio mixtures using the audio-visual coherence. It consists of two stages: in the off-line training process, we use the Gaussian mixture model to characterise statistically the audio- visual coherence with features obtained from the training set; at the separation stage, likelihood maximization is performed on the independent component analysis (ICA)-separated spectral components. To address the permutation and scaling indeterminacies of the frequency-domain blind source separation (BSS), a new sorting and rescaling scheme using the bimodal coherence is proposed. We tested our algorithm on the XM2VTS database, and the results show that our algorithm can address the permutation problem with high accuracy, and mitigate the scaling problem effectively.