Au sein de cette démonstration, nous présentons Muzeeglot, une interface web permettant de visualiser des espaces de représentations de genres musicaux provenant de sources variées et de langues différentes. Nous montrons l’efficacité de notre système à prédire automatiquement les genres correspondant à une entité musicale (titre, artiste, album...) selon une certaine source ou langue, étant données des annotations provenant de sources ou de langues différentes.

The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.