- Recherche et sélection de publications
|
Multichannel Audio Source Separation with Probabilistic Reverberation Priors
- Simon Leglaive #1, Roland Badeau #1, Gaël Richard #1
-
| #1 |
Laboratoire Traitement et Communication de l'Information [Paris] (LTCI)
- Télécom ParisTech
- CNRS : UMR5141
|
- References
- IEEE Transactions on Audio, Speech and Language Processing, December 2016, vol. 24, n° 12, pp. 2453-2465
- Abstract
Incorporating prior knowledge about the sources and/or the mixture is a way to improve under-determined audio source separation performance. A great number of informed source separation techniques concentrate on taking priors on the sources into account, but fewer works have focused on constraining the mixing model. In this paper we address the problem of under-determined multichannel audio source separation in reverberant conditions. We target a semi-informed scenario where some room parameters are known. Two probabilistic priors on the frequency response of the mixing filters are proposed. Early reverberation is characterized by an autoregressive model while according to statistical room acoustics results, late reverberation is represented by an autoregressive moving average model. Both reverberation models are defined in the frequency domain. They aim to transcribe the temporal characteristics of the mixing filters into frequency-domain correlations. Our approach leads to a maximum a posteriori estimation of the mixing filters which is achieved thanks to an expectation-maximization algorithm. We experimentally show the superiority of this approach compared with a maximum likelihood estimation of the mixing filters.
- Keywords
- Multichannel audio source separation, probabilistic priors, mixing model, MAP estimation, EM algorithm
- Category
- Article in peer reviewed Journal
- Research Area(s)
- Engineering Sciences/Signal and Image processing
- Identifier(s)
-
Bibliographic key SL:TASLP-16
- File(s)
-
- Export
-
- Last update
- on december 08, 2016 by Roland Badeau
|
