In live multitrack recordings, each voice is usually captured by dedicated close microphones. Unfortunately, it is also captured in practice by other microphones intended for other sources, leading to so-called “interferences”. Reducing this leakage is desirable because it opens new perspectives for the engineering of live recordings. Hence, it has been the topic of recent research in audio processing. In this paper, we show how a Gaussian probabilistic framework may be set up for obtaining good isolation of the target sources. Doing so, we extend several state-of-the art methods by fixing some heuristic parts of their algorithms. As we show in a perceptual evaluation on real-world multitrack live recordings, the resulting principled techniques yield improved quality.
Di Carlo, Diego; Déguernel, Ken; Liutkus, Antoine
Affiliations: INRIA, Villers-les-Nancy, France; STMS Lab IRCAM/CNRS/UPMC, Paris, France(See document for exact affiliation information.)
AES Conference: 2017 AES International Conference on Semantic Audio (June 2017)
Paper Number: 1-1
Publication Date: June 13, 2017
Subject: Audio Source Separation
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