Real-time independent vector analysis with Student's t source prior for convolutive speech mixtures
Jack Harris, Bertrand Rivet, Syed Mohsen Naqvi, Jonathon A. Chambers, Christian Jutten
Abstract
A common approach to blind source separation is to use independent component analysis. However when dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in independent component analysis, across the frequency bins. Independent vector analysis directly addresses this issue by modeling the dependencies between frequency bins, namely making use of a source prior. An alternative source prior for real-time (online) natural gradient independent vector analysis is proposed. A Student's t probability density function is known to be more suited for speech sources, due to its heavier tails, and is incorporated into a real-time version of natural gradient independent vector analysis. In addition, the importance of the degrees of freedom parameter within the Student's t distribution is highlighted. The final algorithm is realized as a real-time embedded application on a floating point Texas Instruments digital signal processor platform, where simulated recordings from a reverberant room are used for testing. Results are shown to be better than with the original (super-Gaussian) source prior.
BibTeX
@inproceedings{icassp2015_realtimeindepend,
title = {Real-time independent vector analysis with Student's t source prior for convolutive speech mixtures},
author = {Jack Harris and Bertrand Rivet and Syed Mohsen Naqvi and Jonathon A. Chambers and Christian Jutten},
booktitle = {ICASSP 2015},
year = {2015}
}