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We propose a novel method to determine crosstalk regions based on the pitch frequency in convolutive blind audio source separation. Since the same pitch frequency in a frame of BSS outputs represents one speaker talking in all separated speech signal, all signals except the speaker's signal contain the crosstalk in that frame. These frames are used in our post-processing algorithm to estimate the...
In this paper, we proposed the approach which combines inverse filter criteria with non-Gaussianity to separate convolutive mixtures of speech in the time domain. In this case, the proposed method first extract innovation processes of speech sources by non-Gaussianity maximization and then artificially color them by re-coloration filters. Computer simulation experiments are presented to illustrate...
This paper presents a novel low complexity time domain algorithm for blind separation of speech signal from their convolutive mixtures. We try to reduce intrinsic computational complexity of time domain algorithms by adapting only a small subset of taps from separating FIR filters which are expected to attain largest values. This selection is accomplished by recovering spatial dependencies using linear...
In this paper, we propose a new blind multichannel adaptive filtering scheme, which incorporates a partial-updating mechanism in the error gradient of the update equation. The proposed blind processing algorithm operates in the time-domain by updating only a selected portion of the adaptive filters. The algorithm steers all computational resources to filter taps having the largest magnitude gradient...
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