Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation |
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Authors: | Leandro Ezequiel Di Persia Diego Humberto Milone Masuzo Yanagida |
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Affiliation: | 1.Facultad de Ingeniería y Ciencias Hídricas,Universidad Nacional del Litoral,Santa Fe,Argentina;2.Department of Intelligent Information Engineering and Science,Doshisha University,Kyoto,Japan |
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Abstract: | In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources
separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to
identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency
Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are
optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which
gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance
using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation
of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the
separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic
speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability
of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech
recognition system. |
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