Abstract
The problem of blind source separation (BSS) of convolved acoustic signals is of great interest for many classes of applications. Due to the convolutive mixing process, the source separation is performed in the frequency domain, using independent component analysis (ICA). However, frequency domain BSS involves several major problems that must be solved. One of these is the permutation problem. The permutation ambiguity of ICA needs to be resolved so that each separated signal contains the frequency components of only one source signal. This article presents a class of methods for solving the permutation problem based on information theoretic distance measures. The proposed algorithms have been tested on different real-room speech mixtures with different reverberation times in conjunction with different ICA algorithms.
Highlights
Blind source separation (BSS) is a technique of recovering the source signals using only observed mixtures when both the mixing process and the sources are unknown
Many state-of-the-art unmixing methods of acoustic signals are based on independent component analysis (ICA) in the frequency domain, where the convolutions of the source signals with the room impulse response are reduced to multiplications with the corresponding transfer functions
The approach is based on the assumption that magnitudes of speech signals adhere to a Rayleigh distribution and the logarithms of magnitudes can be modeled by a generalized Gaussian distribution (GGD)
Summary
Blind source separation (BSS) is a technique of recovering the source signals using only observed mixtures when both the mixing process and the sources are unknown. Due to a large number of applications for example in medical and speech signal processing, BSS has gained great attention. This article considers the case of BSS for acoustic signals observed in a real environment, i.e., convolutive mixtures, focusing on speech signals in particular. Many state-of-the-art unmixing methods of acoustic signals are based on independent component analysis (ICA) in the frequency domain, where the convolutions of the source signals with the room impulse response are reduced to multiplications with the corresponding transfer functions. For each frequency bin, an individual instantaneous ICA problem arises [2]
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