Publication detail

Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

MOKRÝ, O. MAGRON, P. OBERLIN, T. FÉVOTTE, C.

Original Title

Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Alternating minimization; Audio inpainting; Expectation–maximization; Nonnegative matrix factorization

Authors

MOKRÝ, O.; MAGRON, P.; OBERLIN, T.; FÉVOTTE, C.

Released

24. 12. 2022

Publisher

Elsevier

Location

Amsterdam, Nizozemsko

ISBN

1872-7557

Periodical

SIGNAL PROCESSING

Number

206

State

Kingdom of the Netherlands

Pages from

1

Pages to

10

Pages count

10

URL

https://www.sciencedirect.com/science/article/pii/S0165168422004443

BibTex

@article{BUT180524,
  author="Ondřej {Mokrý} and Paul {Magron} and Thomas {Oberlin} and Cédric {Févotte}",
  title="Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization",
  journal="SIGNAL PROCESSING",
  year="2022",
  number="206",
  pages="1--10",
  doi="10.1016/j.sigpro.2022.108905",
  issn="1872-7557",
  url="https://www.sciencedirect.com/science/article/pii/S0165168422004443"
}