Unsupervised Source Separation via Bayesian Inference in the Latent Domain

• URL: http://arxiv.org/abs/2110.05313v1
• Date: Mon, 11 Oct 2021 14:32:55 GMT
• Title: Unsupervised Source Separation via Bayesian Inference in the Latent Domain
• Authors: Michele Mancusi, Emilian Postolache, Marco Fumero, Andrea Santilli, Luca Cosmo, Emanuele Rodol\`a
• Abstract summary: State of the art audio source separation models rely on supervised data-driven approaches. We propose a simple yet effective unsupervised separation algorithm, which operates directly on a latent representation of time-domain signals. We validate our approach on the Slakh dataset arXiv:1909.08494, demonstrating results in line with state of the art supervised approaches.
• Score: 4.583433328833251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources. On the other hand, approaches for training these models without any direct supervision are typically high-demanding in terms of memory and time requirements, and remain impractical to be used at inference time. We aim to tackle these limitations by proposing a simple yet effective unsupervised separation algorithm, which operates directly on a latent representation of time-domain signals. Our algorithm relies on deep Bayesian priors in the form of pre-trained autoregressive networks to model the probability distributions of each source. We leverage the low cardinality of the discrete latent space, trained with a novel loss term imposing a precise arithmetic structure on it, to perform exact Bayesian inference without relying on an approximation strategy. We validate our approach on the Slakh dataset arXiv:1909.08494, demonstrating results in line with state of the art supervised approaches while requiring fewer resources with respect to other unsupervised methods.

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