Title :
Adaptive Sparse Source Separation with Application to Speech Signals
Author :
Azizi, Elham ; Mohimani, G. Hosein ; Babaie-Zadeh, Massoud
Author_Institution :
Electr. Eng. Dept., Sharif Univ. of Technol., Tehran, Iran
Abstract :
In this paper, a sparse component analysis algorithm is presented for the case in which the number of sources is less than or equal to the number of sensors, but the channel (mixing matrix) is time-varying. The method is based on a smoothed ¿0 norm for the sparsity criteria, and takes advantage of the idea that sparsity of the sources is decreased when they are mixed. The method is able to separate synthetic and speech data, which require very weak sparsity restrictions. It can separate up to 50 mixed signals while being adaptive to channel variation and robust against noise.
Keywords :
adaptive signal processing; blind source separation; independent component analysis; smoothing methods; sparse matrices; speech processing; time-varying channels; adaptive sparse source separation; channel noise; mixture matrix; smoothing method; sparse component analysis algorithm; speech signals; time-varying channel; Adaptive signal processing; Blind source separation; Frequency; Independent component analysis; Multiple signal classification; Noise robustness; Signal processing algorithms; Source separation; Sparse matrices; Speech analysis; Adaptive Source Separation; Blind Source Separation; Smoothed l0 Norm; Sparse Component Analysis;
Conference_Titel :
Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-1-4244-1235-8
Electronic_ISBN :
978-1-4244-1236-5
DOI :
10.1109/ICSPC.2007.4728400
