SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION - Archive ouverte HAL Access content directly
Conference Papers Year :

SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION

Victor Bisot
  • Function : Author
Romain Serizel
Gael Richard

Abstract

This report describes our contribution to the 2016 IEEE AASP DCASE challenge for the acoustic scene classification task. We propose a feature learning approach following the idea of decomposing time-frequency representations with nonnegative matrix factoriza-tion. We aim at learning a common dictionary representing the data and use projections on this dictionary as features for classification. Our system is based on a novel supervised extension of nonnegative matrix factorization. In the approach we propose, the dictionary and the classifier are optimized jointly in order to find a suited representation to minimize the classification cost. The proposed method significantly outperforms the baseline and provides improved results compared to unsupervised nonnegative matrix factorization.
Fichier principal
Vignette du fichier
VB_DCASE-16.pdf (270.41 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02943480 , version 1 (23-09-2020)

Identifiers

  • HAL Id : hal-02943480 , version 1

Cite

Victor Bisot, Romain Serizel, Slim Essid, Gael Richard. SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION. IEEE international evaluation campaign on detection and classification of acousitc scenes and events (DCASE 2016), Sep 2016, Budapest, Hungary. ⟨hal-02943480⟩
92 View
112 Download

Share

Gmail Facebook Twitter LinkedIn More