HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry

TL;DR

This paper introduces HouseX, a detailed house music dataset with sub-genre labels, and demonstrates its effectiveness in genre classification and potential applications in the music industry.

Contribution

It provides a new fine-grained house music dataset with sub-genre annotations and baseline classification models, enabling more precise genre recognition.

Findings

Baseline models achieve competitive classification accuracy.

Annotations effectively distinguish different sub-genres.

Application demo shows potential in real-world scenarios.

Abstract

Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.