ALIGNMENT KERNELS FOR AUDIO CLASSIFICATION WITH APPLICATION TO MUSIC INSTRUMENT RECOGNITION - Télécom Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2008

ALIGNMENT KERNELS FOR AUDIO CLASSIFICATION WITH APPLICATION TO MUSIC INSTRUMENT RECOGNITION

Cyril Joder
  • Fonction : Auteur
  • PersonId : 1077526
Gaël Richard

Résumé

In this paper we study the efficiency of support vector machines (SVM) with alignment kernels in audio classification. The classification task chosen is music instrument recognition. The alignment kernels have the advantage of handling sequential data, without assuming a model for the probability density of the features as in the case of Gaussian Mixture Model-based Hidden Markov Models (HMM). These clas-sifiers are compared to several reference systems, namely Gaussian Mixture Model, HMM classifiers and SVMs with "static" kernels. Using a higher-level representation of the feature sequence, which we call summary sequence, we show that the use of alignment kernels can significantly improve the classification scores in comparison to the reference systems .
Fichier principal
Vignette du fichier
CJ_EUSIPCO-08.pdf (192.26 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02943674 , version 1 (20-09-2020)

Identifiants

  • HAL Id : hal-02943674 , version 1

Citer

Cyril Joder, Slim Essid, Gaël Richard. ALIGNMENT KERNELS FOR AUDIO CLASSIFICATION WITH APPLICATION TO MUSIC INSTRUMENT RECOGNITION. 16th European Signal Processing Conference, Aug 2008, Lausanne, Switzerland. ⟨hal-02943674⟩
21 Consultations
44 Téléchargements

Partager

Gmail Facebook X LinkedIn More