This paper presents a method for blind separation of convolutive mixtures of speech signals, based on the joint diagonalization of the time varying spectral matrices of the observation records and a novel technique to handle the problem of permutation ambiguity in the frequency domain. Simulations show that our method works well even for rather realistic mixtures in which the mixing filter has a quite long impulse response and strong echos.
In this paper, we propose a frequency-domain approach for convolutive blind separation of nonstationary acoustic sources (ConvBSS). We focus on the challenging problem of permutation ambiguity correction and introduce a new method using spectrum characterization of acoustic signals. The proposed approach involves the computation of the harmonic product spectrum (HPS) to take advantage of the sources' time-frequency representation. The permutation ambiguity problem is then resolved by searching the permutation that maximizes the HPS-based correlation between the constructed harmonic spectrum and the estimated one at each resolution step. We demonstrate through experiment simulations that the ConvBSS is achieved in real environments.
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