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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
