Underdetermined source separation using time-frequency masks and an adaptive combined Gaussian-Student's t probabilistic model
Yang Sun, Waqas Rafique, Jonathon A. Chambers, Syed Mohsen Naqvi
Abstract
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.
BibTeX
@inproceedings{icassp2017_underdetermineds,
title = {Underdetermined source separation using time-frequency masks and an adaptive combined Gaussian-Student's t probabilistic model},
author = {Yang Sun and Waqas Rafique and Jonathon A. Chambers and Syed Mohsen Naqvi},
booktitle = {ICASSP 2017},
year = {2017}
}