Multichannel Audio Source Separation with Probabilistic Reverberation Priors

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.
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Simon Leglaive, Roland Badeau, Gaël Richard. Multichannel Audio Source Separation with Probabilistic Reverberation Priors. IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2016, 24 (12), pp.2453-2465. ⟨hal-01370051⟩

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