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We address the problem of blind audio source separation in the under-determined and convolutive case. The contribution of each source to the mixture channels in the time-frequency domain is modeled by a zero-mean Gaussian random vector with a full rank covariance matrix composed of two terms: a variance which represents the spectral properties of the source and which is modeled by a nonnegative matrix factorization (NMF) model and another full rank covariance matrix which encodes the spatial properties of the source contribution in the mixture. We address the estimation of these parameters by maximizing the likelihood of the mixture using an expectation-maximization (EM) algorithm. Theoretical propositions are corroborated by experimental studies on stereo reverberant music mixtures.

This paper addresses the challenging task of single channel audio source separation. We introduce a novel concept of on the-fly audio source separation which greatly simplifies the user's interaction with the system compared to the state-of the-art user-guided approaches. In the proposed framework, the user is only asked to listen to an audio mixture and type some keywords (e.g. "dog barking ", "wind ", etc.) describing the sound sources to be separated. These keywords are then used as text queries to search for audio examples from the internet to guide the separation process. In particular, we pro pose several approaches to efficiently exploit these retrieved examples, including an approach based on a generic spectral model with group sparsity-inducing constraints. Finally, we demonstrate the effectiveness of the proposed framework with mixtures containing various types of sounds.Index Terms-On-the-fly source separation, user-guided, non-negative matrix factorization, group sparsity, universal spectral model.