Local-feature-map Integration Using Convolutional Neural Networks for Music Genre Classification - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Local-feature-map Integration Using Convolutional Neural Networks for Music Genre Classification

Toru Nakashika
  • Fonction : Auteur
Tetsuya Takiguchi
  • Fonction : Auteur

Résumé

A map-based approach, which treats 2-dimensional acoustic features like image processing, has recently attracted attention in music genre classification. While this is successful at extracting local music-patterns compared with other timbral-feature-based methods, the extracted features are not sufficient for music genre classification. In this paper, we focus on appropriate feature extraction and proper classification by integrating the features. For the musical feature extraction, we calculate gray level co-occurrence matrix (GLCM) descriptors with different offsets from a short-term mel spectrogram. These feature maps are integratively classified using convolutional neural network (CNN). In our experiments, we got a large improvement of more than 10 points in the classification accuracy, compared with conventional map-based methods. Index Terms: music genre classification, music infor- mation retrieval, music feature extraction, convolutional neural network
Fichier non déposé

Dates et versions

hal-01353004 , version 1 (10-08-2016)

Identifiants

  • HAL Id : hal-01353004 , version 1

Citer

Toru Nakashika, Christophe Garcia, Tetsuya Takiguchi. Local-feature-map Integration Using Convolutional Neural Networks for Music Genre Classification. 13th Annual Conference of the International Speech Communication Association (INTERSPEECH 2012), Sep 2012, Portland, Oregon, United States. pp.1752-1755. ⟨hal-01353004⟩
124 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More