| Paper: | MLSP-P1.2 | ||
| Session: | Blind Source Separation II | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | Wavelet based independent component analysis for multi-channel source separation | ||
| Authors: | Rachid Moussaoui, Jean Rouat, Roch Lefebvre, Université de Sherbrooke, Canada | ||
| Abstract: | We consider the problem of separating instantaneous mixtures of different sound sources in multi-channel audio signals. Several methods have been developed to solve this problem. Independent component analysis (ICA) is certainly the most known method and the most used. ICA exploits the non-Gaussianity of the sources in the mixtures. In this study, we propose an improved signal separation algorithm where simultaneously we increase the non-Gaussian signals of signals and we initiate the preliminary separation. For this, the observations are transformed into an adequate representation using the wavelet packets decomposition. In this study, we consider the instantaneous mixture of two sources using two sensors. We validate our approach by using synthetic and recorded audio signals. Preliminary results show a strong improvement when compared to conventional ICA (FastICA), with specific signals. | ||
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