This paper presents a contrast function and associated algorithm for blind separation of audio signals. The contrast function is based on second-order statistics to minimize the ratio between the product of the diagonal entries and the determinant of the covariance matrix. The contrast function can be minimized by a batch and adaptive gradient descent method to formulate a blind source separation algorithm. Experimental results on realistic audio signals show that the proposed algorithm yielded comparable separation performance with benchmark algorithms for speech signals, and outperformed benchmark algorithms for music signals.
Bounded Component Analysis (BCA) solves the Blind Source Separation (BSS) problem based on geometric assumptions. This paper introduces a new proof of a BCA contrast function, derived from elementary matrices, Gauss-Jordan elimination and convex geometry. The new proof and further analysis provide additional insight into a key assumption of BCA. In addition, an interpretation is presented to clarify one of the limitations of the instantaneous BCA algorithm. Experiments on audio sources support our analysis.
Aimed to overcome the deficiency of abundant data to web mining, the paper proposed an association-analysis based algorithm. Firstly, we construct the relation Information System using original data sets. Secondly, make use of attribute reduction theory of Rough sets to produce the Core of Information System. Core is the most important and necessary information which cannot reduce in original Information System. So it can get a same effect as original data sets to data analysis, and can construct classification modeling using it. Thirdly, construct indiscernibility matrix using reduced Information System, and finally, get the classification of original data sets. The experiments shows that the proposed algorithm can get high efficiency and can avoid the abundant data in follow-up data processing.
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