Underdetermined source separation using time-frequency masks and an adaptive combined Gaussian-Student's t probabilistic model

Sun, Y; Rafique, W; Chambers, JA; Naqvi, SM

doi:10.1109/ICASSP.2017.7952945

Underdetermined source separation using time-frequency masks and an adaptive combined Gaussian-Student's t probabilistic model

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

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Abstract

© 2017 IEEE. Time-frequency (T-F) masking algorithms are focused at separating multiple sound sources from binaural reverberant speech mixtures. The statistical modelling of binaural cues i.e. interaural phase difference (IPD) and interaural level difference (ILD) is a significant aspect of such algorithms. In this paper, a Gaussian-Student's t distribution combined mixture model is exploited for robust binaural speech separation. The weights of the distribution components are calculated adaptively with the energy of the speech mixtures. The expectation maximization (EM) algorithm is applied to calculate the parameters of the distributions. The speech signals from the TIMIT database are convolved with the real binaural room impulse responses (BRIRs) from two datasets for the evaluation of the proposed method. The objective performance measure signal to distortion ratio (SDR) confirms the improvement and robustness of the proposed method.