Estimation with Low-Rank Time-Frequency Synthesis Models

Abstract : Many state-of-the art signal decomposition techniques rely on a low-rank factorization of a time-frequency (t-f) transform. In particular, nonnegative matrix factorization (NMF) of the spectrogram has been considered in many audio applications. This is an analysis approach in the sense that the factorization is applied to the squared magnitude of the analysis coefficients returned by the t-f transform. In this paper we instead propose a synthesis approach, where low-rankness is imposed to the synthesis coefficients of the data signal over a given t-f dictionary (such as a Gabor frame). As such we offer a novel modeling paradigm that bridges t-f synthesis modeling and traditional analysis-based NMF approaches. The proposed generative model allows in turn to design more sophisticated multi-layer representations that can efficiently capture diverse forms of structure. Additionally, the generative modeling allows to exploit t-f low-rankness for compressive sensing. We present efficient iterative shrinkage algorithms to perform estimation in the proposed models and illustrate the capabilities of the new modeling paradigm over audio signal processing examples.
Type de document :
Pré-publication, Document de travail
2018
Liste complète des métadonnées

Littérature citée [32 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01680655
Contributeur : Matthieu Kowalski <>
Soumis le : jeudi 11 janvier 2018 - 00:04:14
Dernière modification le : vendredi 15 juin 2018 - 01:18:44

Fichier

tsp_lrtfs.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01680655, version 1

Citation

Cédric Févotte, Matthieu Kowalski. Estimation with Low-Rank Time-Frequency Synthesis Models. 2018. 〈hal-01680655v1〉

Partager

Métriques

Consultations de la notice

223

Téléchargements de fichiers

143