Title :
Independent component analysis using time delayed sampling
Author :
Yoshioka, Michifumi ; Omatu, Sigeru
Author_Institution :
Coll. of Eng., Osaka Prefecture Univ., Japan
Abstract :
In recent years, growing multimedia systems require more efficient signal separation methods to preserve the quality of voice or music recording under a noisy environment. Some of signal separation methods are based on minimizing the dependent measure among input signals to separate a noise component since a noise component is usually independent on the other signals. Under such circumstances, we have developed a new method to separate independent signal components which directly minimizes the Kullback-Leibler divergence by a genetic algorithm (GA). In this paper, we have proposed an improved method using differential information. As the result of the simulation, the separated signals are clearly separated to original signals
Keywords :
acoustic signal processing; delays; genetic algorithms; minimisation; multimedia systems; neural nets; principal component analysis; signal sampling; GA; Kullback-Leibler divergence; dependent measure minimization; differential information; efficient signal separation methods; genetic algorithm; independent component analysis; independent signal components; multimedia systems; music recording quality; noise component separation; noisy environment; time delayed sampling; voice recording quality; Blind source separation; Computational modeling; Delay effects; Equations; Independent component analysis; Noise measurement; Probability density function; Sampling methods; Source separation; Working environment noise;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860752