Music feature maps with convolutional neural networks for music genre classification

Christine Sénac (1) , Thomas Pellegrini (1) , Florian Mouret (2) , Julien Pinquier (1)

1 IRIT-SAMoVA - Équipe Structuration, Analyse et MOdélisation de documents Vidéo et Audio
2 IRIT-SC - Signal et Communications

Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure. But, facing the criticism relating to the difficulty in understanding the underlying relationships that neural networks learn in presence of a spectrogram, we propose to use, as entries of a CNN, a small set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality. With CNNs trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are more efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types reaches 91% accuracy on the GTZAN database.

Mots clés: Computing methodologies; Machine learning; Machine learning approaches; Neural networks

Domaines: Intelligence artificielle [cs.AI]

Christine Sénac, Thomas Pellegrini, Florian Mouret, Julien Pinquier. Music feature maps with convolutional neural networks for music genre classification. International Workshop on Content-Based Multimedia Indexing (CBMI), Jun 2017, Florence, Italy. pp.1-5, ⟨10.1145/3095713.3095733⟩. ⟨hal-02874986⟩