Indeterminacy free frequency-domain blind separation of reverberant audio sources

  • Authors:

  • Leandro Di Persia;Diego Milone;Masuzo Yanagida

  • Affiliations:

  • National University of Litoral and CONICET, Facultad de Ingeniería y Ciencias Hídricas and Ciudad Universitaria, Santa Fe, Argentina;National University of Litoral and CONICET, Facultad de Ingeniería y Ciencias Hídricas and Ciudad Universitaria, Santa Fe, Argentina;Intelligent Information Engineering and Science Department, Engineering Faculty, Doshisha University, Kyo-Tanabe, Japan

  • Venue:
  • IEEE Transactions on Audio, Speech, and Language Processing
  • Year:
  • 2009

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Abstract

Blind separation of convolutive mixtures is a very complicated task that has applications in many fields of speech and audio processing, such as hearing aids and man-machine interfaces. One of the proposed solutions is the frequency-domain independent component analysis. The main disadvantage of this method is the presence of permutation ambiguities among consecutive frequency bins. Moreover, this problem is worst when reverberation time increases. Presented in this paper is a new frequency-domain method, that uses a simplified mixing model, where the impulse responses from one source to each microphone are expressed as scaled and delayed versions of one of these impulse responses. This assumption, based on the similitude among waveforms of the impulse responses, is valid for a small spacing of the microphones. Under this model, separation is performed without any permutation or amplitude ambiguity among consecutive frequency bins. This new method is aimed mainly to obtain separation, with a small reduction of reverberation. Nevertheless, as the reverberation is included in the model, the new method is capable of performing separation for a wide range of reverberant conditions, with very high speed. The separation quality is evaluated using a perceptually designed objective measure. Also, an automatic speech recognition system is used to test the advantages of the algorithm in a real application. Very good results are obtained for both, artificial and real mixtures. The results are significantly better than those by other standard blind source separation algorithms.