This work is focused on the processing of multichannel and multisource audio signals. From an audio mixture of several audio sources recorded in a reverberant room, we wish to estimate the acoustic responses (a.k.a. mixing filters) between the sources and the microphones. To solve this inverse problem one need to take into account additional hypotheses on the nature of the acoustic responses. Our approach consists in first identifying mathematically the necessary hypotheses on the acoustic responses for their estimation and then building cost functions and algorithms to effectively estimate them. First, we considered the case where the source signals are known. We developed a method to estimate the acoustic responses based on a convex regularization which exploits both the temporal sparsity of the filters and the exponentially decaying envelope. Real-world experiments confirmed the effectiveness of this method on real data. Then, we considered the case where the sources signal are unknown, but statistically independent. The mixing filters can be estimated up to a permutation and scaling ambiguity. We brought up an exhaustive study of the theoretical conditions under which we can solve the indeterminacy, when the multichannel filters are sparse in the temporal domain. Finally, we started to analyse the hypotheses under which this algorithm could be extended to the joint estimation of the sources and the filters, and showed a first unexpected results : in the context of blind deconvolution with sparse priors, for a quite large family of regularised cost functions, the global minimum is trivial. Additional constraints on the source signals and the filters are needed.
