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

Lookup NU author(s): Dr Mohsen Naqvi, Professor Jonathon Chambers


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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).

Publication metadata

Author(s): Zohny Z, Naqvi SM, Chambers JA

Publication type: Article

Publication status: Published

Journal: IET Electronics Letters

Year: 2014

Volume: 50

Issue: 7

Pages: 552-554

Print publication date: 27/03/2014

ISSN (print): 0013-5194

Publisher: The Institution of Engineering and Technology


DOI: 10.1049/el.2013.4230


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