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Estimation of the Number of Sources in Measured Speech Mixtures with Collapsed Gibbs Sampling

Lookup NU author(s): Dr Yang Sun, Pengming Feng, Professor Jonathon Chambers, Dr Mohsen Naqvi


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© 2017 IEEE. In blind source separation (BSS), the number of sources present in the measured speech mixtures is unknown. The focus of this work is therefore to automatically estimate the number of sources from binaural speech mixtures. Collapsed Gibbs sampling (CGS), a Markov chain Monte Carlo (MCMC) technique, is used to obtain samples from the joint distribution of the speech mixtures. Then the Chinese Restaurant Process (CRP) within the framework of the Dirichlet Process (DP) is exploited to cluster samples into different components to finally estimate the number of speakers. The accuracy of the proposed method, under different reverberant environments, is evaluated with real binaural room impulse responses (BRIRs) and speech signals from the TIMIT database. The experimental results confirm the accuracy and robustness of the proposed method.

Publication metadata

Author(s): Sun Y, Xian Y, Feng P, Chambers JA, Naqvi SM

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Sensor Signal Processing for Defence Conference (SSPD)

Year of Conference: 2017

Online publication date: 21/12/2017

Acceptance date: 02/04/2016

Publisher: IEEE


DOI: 10.1109/SSPD.2017.8233232

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538616635