Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

Ondřej Mokrý (1) , Paul Magron (2) , Thomas Oberlin (3) , Cédric Févotte (4, 5)

1 BUT - Brno University of Technology [Brno]
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
3 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
4 IRIT-SC - Signal et Communications
5 CNRS - Centre National de la Recherche Scientifique

Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization (NMF) in a probabilistic framework. First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time-or time-frequency domain. Then, we treat the missing samples as parameters, and we address this novel problem by deriving an alternating minimization scheme. We assess the potential of these algorithms for the task of restoring short-to middle-length gaps in music signals. Experiments reveal great convergence properties of the proposed methods, as well as competitive performance when compared to state-of-the-art audio inpainting techniques.

Mots clés: alternating minimization; audio inpainting; expectation-maximization; nonnegative matrix factorization

Domaines: Traitement du signal et de l'image [eess.SP]

Ondřej Mokrý, Paul Magron, Thomas Oberlin, Cédric Févotte. Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization. 2022. ⟨hal-03708613v1⟩