@inproceedings{bbb93eedb5a44889b34d9f6a6fecb78a,
title = "Multichannel audio source separation with probabilistic reverberation modeling",
abstract = "In this paper we show that considering early contributions of mixing filters through a probabilistic prior can help blind source separation in reverberant recording conditions. By modeling mixing filters as the direct path plus R-1 reflections, we represent the propagation from a source to a mixture channel as an autoregressive process of order R in the frequency domain. This model is used as a prior to derive a Maximum A Posteriori (MAP) estimation of the mixing filters using the Expectation-Maximization (EM) algorithm. Experimental results over reverberant synthetic mixtures and live recordings show that MAP estimation with this prior provides better separation results than a Maximum Likelihood (ML) estimation.",
keywords = "Blind audio source separation, EM algorithm, MAP estimation, Probabilistic prior, Under-determined convolutive mixtures",
author = "Simon Leglaive and Roland Badeau and Gael Richard",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015 ; Conference date: 18-10-2015 Through 21-10-2015",
year = "2015",
month = nov,
day = "24",
doi = "10.1109/WASPAA.2015.7336932",
language = "English",
series = "2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015",
}