Modeling the Music Genre Perception across Language-Bound Cultures

TL;DR

This paper investigates the feasibility of cross-lingual, culture-specific music genre annotation using semantic representations, demonstrating high accuracy and introducing a new multilingual corpus for benchmarking.

Contribution

It presents the most extensive study to date on cross-lingual music genre annotation using semantic embeddings and introduces a new corpus for benchmarking multilingual models.

Findings

Unsupervised cross-lingual annotation achieves high accuracy.

Combining concept embeddings and ontologies improves results.

The new corpus benchmarks state-of-the-art multilingual models.

Abstract

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.