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Communication Dans Un Congrès Année : 2016

SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION

Victor Bisot
  • Fonction : Auteur
Romain Serizel
Gael Richard

Résumé

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.
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Dates et versions

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

Identifiants

  • HAL Id : hal-02943480 , version 1

Citer

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⟩
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