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In this paper, minimization of the statistical dependence is exploited for acoustic source localization purposes. Originally developed for the separation of signal mixtures, we show that Independent Component Analysis (ICA) can also be successfully applied to localize multiple simultaneously active sound sources, with possibly less sensors than sources. First, the recently proposed Averaged Directivity...
In this paper, we show that minimization of the statistical dependence using broadband independent component analysis (ICA) can be successfully exploited for acoustic source localization. As the ICA signal model inherently accounts for the presence of several sources and multiple sound propagation paths, the ICA criterion offers a theoretically more rigorous framework than conventional techniques...
Recently, methods using blind signal separation were proposed to separate the signals received by a microphone array. In this paper, we propose a new frequency domain semi-blind source separation method for replacing the blind source separation method when it is possible to obtain additional information on some of the signals. This is of particular interest in situations like in hands-free speech...
This paper presents a new approach for multiple speaker DOA estimation using an array of microphones. The method relies on the fact that multiple independent speakers have a small overlap in the time-frequency domain, i.e. the individual signals are almost W-disjoint orthogonal. By introducing a time-frequency mask and by continuously tracking the set of time-frequency points corresponding to each...
This paper presents a prototype system for blind source separation (BSS) of many speech signals and describes the techniques used in the system. Our system uses 8 microphones located at the vertexes of a 4 cm times 4 cm times 4 cm cube and has the ability to separate signals distributed in three-dimensional space. The mixed signals observed by the microphone array are processed by independent component...
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