Projection-based demixing of spatial audio

Abstract : We propose a method to unmix multichannel audio signals into their different constitutive spatial objects. To achieve this, we characterize an audio object through both a spatial and a spectro-temporal modelling. The particularity of the spatial model we pick is that it neither assumes an object has only one underlying source point, nor does it attempt to model the complex room acoustics. Instead, it focuses on a listener perspective, and takes each object as the superposition of many contributions with different incoming directions and inter-channel delays. Our spectro-temporal probabilistic model is based on the recently proposed α-harmonisable processes, which are adequate for signals with large dynamics, such as audio. Then, the main originality of this work is to provide a new way to estimate and exploit inter-channel dependences of an object for the purpose of demixing. In the Gaussian α = 2 case, previous research focused on covariance structures. This approach is no longer valid for α < 2 where covariances are not defined. Instead, we show how simple linear combinations of the mixture channels can be used to learn the model parameters, and the method we propose consists in pooling the estimates based on many projections to correctly account for the original multichannel audio. Intuitively, each such downmix of the mixture provides a new perspective where some objects are cancelled or enhanced. Finally, we also explain how to recover the different spatial audio objects when all parameters have been computed. Performance of the method is illustrated on the separation of stereophonic music signals. Index Terms—source separation, probabilistic models, non-negative matrix factorization, musical source separation
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IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2016
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  • HAL Id : hal-01260588, version 2

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Derry Fitzgerald, Antoine Liutkus, Roland Badeau. Projection-based demixing of spatial audio. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2016. 〈hal-01260588v2〉

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