Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity
نویسندگان
چکیده
منابع مشابه
Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity
Blind source separation (BSS) and sound activity detection (SAD) from a sound source mixture with minimum prior information are two major requirements for computational auditory scene analysis that recognizes auditory events in many environments. In daily environments, BSS suffers from many problems such as reverberation, a permutation problem in frequency-domain processing, and uncertainty abo...
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The problem of the blind signal separation (BSS) consists of estimating the latent component signals in a linear mixture, referred to as the sources, starting from several observed signals, without relying on any specific knowledge of the sources. In particular, when the sources are audible, this problem is known as to the cocktail-party problem, making reference to the ability of the human ear...
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Frequency domain blind source separation has the great advantage that the complicated convolution in time domain becomes multiple efficient multiplications in frequency domain. However, the inherent ambiguity of permutation of ICA becomes an important problem that the separated signals at different frequencies may be permuted in order. Mapping the separated signal at each frequency to a target ...
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Existing methods for frequency-domain estimation of mixing filters in convolutive blind source separation (BSS) suffer from permutation and scaling indeterminacies in sub-bands. However, if the filters are assumed to be sparse in the time domain, it is shown in this paper that the !1-norm of the filter matrix increases as the sub-band coefficients are permuted. With this motivation, an algorith...
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ژورنال
عنوان ژورنال: EURASIP Journal on Audio, Speech, and Music Processing
سال: 2013
ISSN: 1687-4722
DOI: 10.1186/1687-4722-2013-4