Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South

TL;DR

This paper analyzes the representation of Global South music in AI music generation research, revealing a significant underrepresentation and discussing steps to promote inclusivity and diversity in the field.

Contribution

It provides an extensive analysis of datasets and research focus, highlighting the imbalance and proposing measures to address the underrepresentation of Global South music genres.

Findings

86% of dataset hours are from the Global North

Only 14.6% of data includes Global South music genres

Over 93% of researchers focus on Global North music

Abstract

Recent advances in generative AI have sparked renewed interest and expanded possibilities for music generation. However, the performance and versatility of these systems across musical genres are heavily influenced by the availability of training data. We conducted an extensive analysis of over one million hours of audio datasets used in AI music generation research and manually reviewed more than 200 papers from eleven prominent AI and music conferences and organizations (AAAI, ACM, EUSIPCO, EURASIP, ICASSP, ICML, IJCAI, ISMIR, NeurIPS, NIME, SMC) to identify a critical gap in the fair representation and inclusion of the musical genres of the Global South in AI research. Our findings reveal a stark imbalance: approximately 86% of the total dataset hours and over 93% of researchers focus primarily on music from the Global North. However, around 40% of these datasets include some form of non-Western music, genres from the Global South account for only 14.6% of the data. Furthermore, approximately 51% of the papers surveyed concentrate on symbolic music generation, a method that often fails to capture the cultural nuances inherent in music from regions such as South Asia, the Middle East, and Africa. As AI increasingly shapes the creation and dissemination of music, the significant underrepresentation of music genres in datasets and research presents a serious threat to global musical diversity. We also propose some important steps to mitigate these risks and foster a more inclusive future for AI-driven music generation.