Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization - Signal et Communications Access content directly
Journal Articles Signal Processing Year : 2023

Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

Abstract

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.
Fichier principal
Vignette du fichier
main.pdf (688.48 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03708613 , version 1 (29-06-2022)
hal-03708613 , version 2 (05-01-2023)

Identifiers

Cite

Ondřej Mokrý, Paul Magron, Thomas Oberlin, Cédric Févotte. Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization. Signal Processing, 2023, ⟨10.1016/j.sigpro.2022.108905⟩. ⟨hal-03708613v2⟩
112 View
103 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More