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Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem...
It is well known that in real world source separation, the environment noise removal must be considered with complex reverberating sound, and various noises. In this study, in order to improve the voice recognition accuracy in real world source separation, a new method that uses independent component analysis (ICA) in the time-frequency domain using the variable density complex discrete wavelet transform...
In this paper we focus on the mixing matrix identification problem for underdetermined blind source separation. Based on the two-stage approach in sparse component analysis, we proposal a new algorithm that integrate with other blind signal processing methods like independent component analysis and model order selection. Compared with the DUET, the TIFROM and standard clustering methods, this algorithm...
Independent Component Analysis (ICA) is a useful method for blind source separation of two signals or more. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT). In this case, the voice and the noise were separated using a new method. At that time, we used the simulation signal. In this study, we analyze measured biological signals by using this...
It is well known that independent component analysis (ICA) is a useful method for blind source separation although it does have some drawbacks, such as performing poorly on unsteady sounds. In this study, in order to improve this deficiency, a new method combining ICA with the complex discrete wavelet transform is proposed and verification of source separation with relation to the problems of permutation...
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