# Audio Based Disambiguation Of Music Genre Tags

arXiv: https://arxiv.org/abs/1809.07256 · HTML: https://mlsanity.com/paper/1809.07256/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [I can listen but cannot read: An evaluation of two-tower multimodal systems for instrument recognition](https://mlsanity.com/paper/2407.18058/) (2024)
- [Learning Unsupervised Hierarchies of Audio Concepts](https://mlsanity.com/paper/2207.11231/) (2022)
- [Music auto-tagging in the long tail: A few-shot approach](https://mlsanity.com/paper/2409.07730/) (2024)
- [Music Playlist Captioning at Scale with Large Language Models](https://mlsanity.com/paper/2606.22460/) (2026)

## Abstract (arXiv)

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.