Detail publikace

Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

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

Originální název

Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

24. 12. 2022

Nakladatel

Elsevier

Místo

Amsterdam, Nizozemsko

ISSN

1872-7557

Periodikum

SIGNAL PROCESSING

Číslo

206

Stát

Nizozemsko

Strany od

1

Strany do

10

Strany počet

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"
}