A new approach to the permutation problem for Blind Source Separation (BSS) in the frequency domain is presented. The independence of the separation across the frequencies, and thus the probability that a permutation may occur, is minimized by a recursive linking of the ICA stage. A recursive adaptive estimation of smooth demixing matrices is used to initialize the Independent Component Analysis (ICA) in order to force it to converge with a coherent permutation across the whole spectrum. Since no information about non stationarity of the signals is exploited, the proposed method works also for short utterances (0.5-1 s) and in highly reverberant environments (T60 ~ 700 ms). Furthermore it is shown that the recursive initialization increases the accuracy of the ICA when a small amount of data observations is available.

Separating short signals in highly reverberant environment by a recursive frequency-domain BSS

Nesta, Francesco;Svaizer, Piergiorgio;Omologo, Maurizio
2008-01-01

Abstract

A new approach to the permutation problem for Blind Source Separation (BSS) in the frequency domain is presented. The independence of the separation across the frequencies, and thus the probability that a permutation may occur, is minimized by a recursive linking of the ICA stage. A recursive adaptive estimation of smooth demixing matrices is used to initialize the Independent Component Analysis (ICA) in order to force it to converge with a coherent permutation across the whole spectrum. Since no information about non stationarity of the signals is exploited, the proposed method works also for short utterances (0.5-1 s) and in highly reverberant environments (T60 ~ 700 ms). Furthermore it is shown that the recursive initialization increases the accuracy of the ICA when a small amount of data observations is available.
2008
9781424423385
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/8653
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