Ibrahim Missaoui

Ibrahim Missaoui, Zied Lachiri

In this paper, we propose a novel separation system for extracting two speech signals from two microphone recordings. Our system combines the blind source separation technique with cepstral smoothing of binary time-frequency masks. The last is composed of two steps. First, the two binary masks are estimated from the separated output signals of BSS algorithm. In the second step, a cepstral smoothing is applied of these spectral masks in order to reduce musical noise typically produced by time-frequency masking. Experiments were carried out with both artificially mixed speech signals using simulated room model and two real recordings. The evaluation results are promising and have shown the effectiveness of our system.

Ibrahim Missaoui, Zied Lachiri

In this paper, we address the problem of blind separation of speech mixtures. We propose a new blind speech separation system, which integrates a perceptual filterbank and independent component analysis (ICA) and using kurtosis criterion. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD) tree in order to accord to critical band characteristics of psycho-acoustic model. Our proposed technique consists on transforming the observations signals into an adequate representation using UWPD and Kurtosis maximization criterion in a new preprocessing step in order to increase the non-Gaussianity which is a pre-requirement for ICA. Experiments were carried out with the instantaneous mixture of two speech sources using two sensors. The obtained results show that the proposed method gives a considerable improvement when compared with FastICA and other techniques.