SINGLE SENSOR SOURCE SEPARATION BASED ON WIENER FILTERING AND MULTIPLE WINDOW STFT
Laurent Benaroya · 2006
The aim of this paper is to investigate the use of multiresolution framework for single sensor source separation based on pseudo-Wiener filtering. We propose a scheme in which the signal is iteratively split in target sources and a residual. Each target source is modeled as the sum of elementary components with known Power Spectral Densities (PSDs). The approach boils down to perform a non negative decomposition of the spectra of the observed signal in a given frame onto the dictionnary of known PSDs. The resolution of the PSDs (and hence the frame length) is changed at each iteration of the algorithm. The decomposition into sources plus residual is done thanks to a confidence measure based on the Fisher information matrix of the expansion coefficients. After theoretical developments we compare the mono and multiresolution approaches and a set of audio examples.