Audio Based Disambiguation Of Music Genre Tags

2018/09/19 by Romain Hennequin, Hennequin, Romain, Jimena Royo-Letelier +3
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Music Technology and Sound Studies #Music and Audio Processing #Speech and Audio Processing #cs.IR

paper · pdf · doi:10.48550/arxiv.1809.07256

published in ISMIR 2018

arxiv created 2018/09/19 · openalex publication_date 2018/09/19 · arxiv updated 2018/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres. We show that such embeddings can be used for disambiguating genre tags (identification of different labels for the same genre, tag translation from a tag system to another, inference of hierarchical taxonomies on these genre tags). These embeddings are built by training a deep convolutional neural network genre classifier with large audio datasets annotated with a flat tag system. We show empirically that they makes it possible to retrieve the original taxonomy of a tag system, spot duplicates tags and translate tags from a tag system to another.

Citations

• ADADELTA: An Adaptive Learning Rate Method
• Automatic tagging using deep convolutional neural networks

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