SHOULD WE CONSIDER THE USERS IN CONTEXTUAL MUSIC AUTO-TAGGING MODELS? - Département Image, Données, Signal Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

SHOULD WE CONSIDER THE USERS IN CONTEXTUAL MUSIC AUTO-TAGGING MODELS?

Elena V Epure
Geoffroy Peeters
Gael Richard

Résumé

Music tags are commonly used to describe and categorize music. Various auto-tagging models and datasets have been proposed for the automatic music annotation with tags. However, the past approaches often neglect the fact that many of these tags largely depend on the user, especially the tags related to the context of music listening. In this paper, we address this problem by proposing a user-aware music auto-tagging system and evaluation protocol. Specifically, we use both the audio content and user information extracted from the user listening history to predict contextual tags for a given user/track pair. We propose a new dataset of music tracks annotated with contextual tags per user. We compare our model to the traditional audio-based model and study the influence of user embeddings on the classification quality. Our work shows that explicitly modeling the user listening history into the automatic tagging process could lead to more accurate estimation of contextual tags.
Fichier principal
Vignette du fichier
ISMIR2020_V3.1.pdf (252.29 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02934433 , version 1 (09-09-2020)

Identifiants

Citer

Karim M Ibrahim, Elena V Epure, Geoffroy Peeters, Gael Richard. SHOULD WE CONSIDER THE USERS IN CONTEXTUAL MUSIC AUTO-TAGGING MODELS?. 21st International Society for Music Information Retrieval Conference, Oct 2020, Montreal, Canada. ⟨10.5281/zenodo.3961560⟩. ⟨hal-02934433⟩
635 Consultations
414 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More