Prevailing Research Areas for Music AI in the Era of Foundation Models

Megan Wei, Mateusz Modrzejewski, Aswin Sivaraman, Dorien Herremans
Proceedings of Machine Learning Research, PMLR 303:1-23, 2026.

Abstract

Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become increasingly mainstream, many researchers in the music AI community may wonder: what research frontiers remain unexplored? This paper outlines several key areas within music AI research that present significant opportunities for further investigation. We begin by examining foundational representation models and highlight emerging efforts toward explainability and interpretability. We then discuss the evolution toward multimodal systems, provide an overview of the current landscape of music datasets and their limitations, and address the growing importance of model efficiency in both training and deployment. Next, we explore applied directions, focusing first on generative models. We review recent systems, their computational constraints, and persistent challenges related to evaluation and controllability. We then examine extensions of these generative approaches to multimodal settings and their integration into artists’ workflows, including applications in music editing, captioning, production, transcription, source separation, performance, discovery, and education. Finally, we explore copyright implications of generative music and propose strategies to safeguard artist rights. While not exhaustive, this survey aims to illuminate promising research directions enabled by recent developments in music foundation models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v303-wei26a, title = {Prevailing Research Areas for Music AI in the Era of Foundation Models}, author = {Wei, Megan and Modrzejewski, Mateusz and Sivaraman, Aswin and Herremans, Dorien}, booktitle = {Proceedings of Machine Learning Research}, pages = {1--23}, year = {2026}, editor = {Herremans, Dorien and Bhandari, Keshav and Roy, Abhinaba and Colton, Simon and Barthet, Mathieu}, volume = {303}, series = {Proceedings of Machine Learning Research}, month = {26 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v303/main/assets/wei26a/wei26a.pdf}, url = {https://proceedings.mlr.press/v303/wei26a.html}, abstract = {Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become increasingly mainstream, many researchers in the music AI community may wonder: what research frontiers remain unexplored? This paper outlines several key areas within music AI research that present significant opportunities for further investigation. We begin by examining foundational representation models and highlight emerging efforts toward explainability and interpretability. We then discuss the evolution toward multimodal systems, provide an overview of the current landscape of music datasets and their limitations, and address the growing importance of model efficiency in both training and deployment. Next, we explore applied directions, focusing first on generative models. We review recent systems, their computational constraints, and persistent challenges related to evaluation and controllability. We then examine extensions of these generative approaches to multimodal settings and their integration into artists’ workflows, including applications in music editing, captioning, production, transcription, source separation, performance, discovery, and education. Finally, we explore copyright implications of generative music and propose strategies to safeguard artist rights. While not exhaustive, this survey aims to illuminate promising research directions enabled by recent developments in music foundation models.} }
Endnote
%0 Conference Paper %T Prevailing Research Areas for Music AI in the Era of Foundation Models %A Megan Wei %A Mateusz Modrzejewski %A Aswin Sivaraman %A Dorien Herremans %B Proceedings of Machine Learning Research %C Proceedings of Machine Learning Research %D 2026 %E Dorien Herremans %E Keshav Bhandari %E Abhinaba Roy %E Simon Colton %E Mathieu Barthet %F pmlr-v303-wei26a %I PMLR %P 1--23 %U https://proceedings.mlr.press/v303/wei26a.html %V 303 %X Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become increasingly mainstream, many researchers in the music AI community may wonder: what research frontiers remain unexplored? This paper outlines several key areas within music AI research that present significant opportunities for further investigation. We begin by examining foundational representation models and highlight emerging efforts toward explainability and interpretability. We then discuss the evolution toward multimodal systems, provide an overview of the current landscape of music datasets and their limitations, and address the growing importance of model efficiency in both training and deployment. Next, we explore applied directions, focusing first on generative models. We review recent systems, their computational constraints, and persistent challenges related to evaluation and controllability. We then examine extensions of these generative approaches to multimodal settings and their integration into artists’ workflows, including applications in music editing, captioning, production, transcription, source separation, performance, discovery, and education. Finally, we explore copyright implications of generative music and propose strategies to safeguard artist rights. While not exhaustive, this survey aims to illuminate promising research directions enabled by recent developments in music foundation models.
APA
Wei, M., Modrzejewski, M., Sivaraman, A. & Herremans, D.. (2026). Prevailing Research Areas for Music AI in the Era of Foundation Models. Proceedings of Machine Learning Research, in Proceedings of Machine Learning Research 303:1-23 Available from https://proceedings.mlr.press/v303/wei26a.html.

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