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Artículo
Centroid-Based Clustering with ab-Divergences
(MDPI AG, 2019-02-19)
Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. ...
Artículo
Initialization method for speech separation algorithms that work in the time-frequency domain
(Acoustical Society of America, 2010)
This article addresses the problem of the unsupervised separa tion of speech signals in realistic scenarios. An initialization procedure is proposed for independent component analysis (ICA) algorithms that work in the ...
Artículo
A Joint Optimization Criterion for Blind DS-CDMA Detection
(Hindawi Publishing Corporation, 2006)
This paper addresses the problem of the blind detection of a desired user in an asynchronous DS-CDMA communications system with multipath propagation channels. Starting from the inverse filter criterion introduced by ...
Artículo
Solving permutations in frequencyy-domain for blind separation of an arbitrary number of speech sources
(Acoustical Society of America, 2011)
Blind separation of speech sources in reverberant environ ments is usually performed in the time-frequency domain, which gives rise to the permutation problem: the different ordering of estimated sources for different ...
Artículo
Centroid-Based Clustering with αβ-Divergences
(MDPI AG, 2019)
Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. ...