Lukthung Classification Using Neural Networks on Lyrics and Audios

TL;DR

This paper presents a neural network-based approach for classifying Lukthung, a traditional Thai music genre, using both lyrics and audio features, achieving high accuracy suitable for personalized music recommendations.

Contribution

It introduces a novel multi-modal neural network model combining lyrics and audio analysis specifically for Lukthung genre classification, addressing the gap in non-western music genre recognition.

Findings

Combined model achieves an F1 score of 0.86.

Outperforms standard classifiers on Lukthung classification.

Effective for personalized Thai music recommendations.

Abstract

Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done on western songs which differ from Thai songs. Lukthung, a distinctive and long-established type of Thai music, is one of the most popular music genres in Thailand and has a specific group of listeners. In this paper, we develop neural networks to classify such Lukthung genre from others using both lyrics and audios. Words used in Lukthung songs are particularly poetical, and their musical styles are uniquely composed of traditional Thai instruments. We leverage these two main characteristics by building a lyrics model based on bag-of-words (BoW), and an audio model using a convolutional neural network (CNN) architecture. We then aggregate the intermediate features learned from both models to build a final classifier. Our results show that the proposed three models outperform all of the standard classifiers where the combined model yields the best $F_1$ score of 0.86, allowing Lukthung classification to be applicable to personalized recommendation for Thai audience.