Title :
Minimum entropy algorithms for source separation
Author :
Wu, Hsiao-Chun ; Principe, Jose C.
Author_Institution :
Lab. of Comput. Neuro-Eng., Florida Univ., Gainesville, FL, USA
Abstract :
The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability density model, a set of general anti-Hebbian rules can be derived. This set of adaptation rules give promising results when we test the real recordings
Keywords :
Gaussian processes; Hebbian learning; maximum likelihood estimation; minimum entropy methods; signal detection; adaptation rules; blind source separation problem; general anti-Hebbian rules; local generalized Gaussian probability density model; maximum likelihood estimation; minimum entropy algorithms; Blind source separation; Entropy; Equations; Finite impulse response filter; Gaussian distribution; Maximum likelihood estimation; Noise reduction; Probability density function; Source separation; Testing;
Conference_Titel :
Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on
Conference_Location :
Notre Dame, IN
Print_ISBN :
0-8186-8914-5
DOI :
10.1109/MWSCAS.1998.759478
