An approach employing signal sparse representation in wavelet domain for underdetermined blind source separation

Several contributions in literature have recently proposed techniques based on assumption of source sparsity in some representation domain to give a solution to the problem of blind source separation in the underdetermined case. This work investigates how to employ wavelet based sparse representation of signals in an already existing algorithm for the problem under study, in order to improve separability of sources, in comparison to application of short time Fourier transform. Different wavelet transforms are considered. Moreover, this approach allows to perform a suitable de-noising operation after the separation algorithm, by thresholding the wavelet coefficients corresponding to extracted sources. This occurs at a very low computational cost, resulting in a further improvement of source recovering when noise is present at mixture level. Experimental results confirm the effectiveness of what implemented.