Abstract
This paper presents a new algorithm for blind source separation (BSS) of instantaneous speech mixtures in under-determined case. A demixing algorithm which exploits the sparsity of speech signals in the short time Fourier transform (STFT) domain is proposed. This algorithm combines the modified k-means clustering procedure involved in the line orientation separation technique (LOST) with Smoothed l0-norm minimization (SL0) method. First procedure along with a transformation into a sparse domain tries to estimate the mixing matrix, and the second method tries to extract the sources from the mixtures. Simulation results are presented and compared to the degenerate unmixing estimation technique (DUET) and LOST algorithms. It is shown in this article that improvements are achieved in two cases. One is the quality of source extraction when the number of mixtures is increased, and second is the speed of source separation when compared to the LOST algorithm. This speed enhancement is about 3 to 10 times comparing to the LOST algorithm. We called the proposed algorithm SL0-LOST.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
