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Enhancing MESSL algorithm with robust clustering based on Student's t-distribution

Enhancing MESSL algorithm with robust clustering based on Student's t-distribution

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The model-based expectation maximisation source separation and localisation (MESSL) algorithm is enhanced through the integration of robust clustering based on the Student's t-distribution. This heavy-tailed distribution, as compared with the Gaussian distribution used in MESSL, can potentially capture in a better manner the outlier values in the univariate parametric modelling of the time–frequency (T–F) points and thereby lead to more accurate probabilistic masks for source separation. In particular, the Student's t-distribution is exploited in modelling the interaural phase difference (IPD) in order to represent in a better manner the uncertainties introduced by the statistical non-stationarity of the speech signals and the associated small sample length effects. Simulation studies based on speech mixtures formed from the TIMIT database confirm the advantage of the proposed approach in terms of the signal to distortion ratio (SDR).

References

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      • 1. Mandel, M.I.: ‘Binaural model-based source separation and localization’, PhD thesis, Columbia University, 2010.
    2. 2)
      • 2. Mandel, M.I., Ellis, D.P.W.: ‘A probability model for interaural phase difference’. Proc. ISCA Workshop Statistical Perceptions Audio Processing (SAPA), Pittsburgh, PA, October 2006, pp. 16.
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      • 6. Garofolo, J.S., et al: ‘DARPA TIMIT acoustic-phonetic continuous speech corpushttp://www.ldc.upenn.edu/Catalog/LDC93S1.html accessed December 2012.
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