@conference {2026,
	title = {Emerging AI Technologies for Music: Towards Controllable, Collaborative, and Creative Systems},
	booktitle = {Proceedings of Machine Learning Research, PMLR 303:1-5, 2026},
	year = {2026},
	abstract = {The field of AI and music has witnessed rapid growth in recent years driving notable success across domains such as music information retrieval (MIR), generative modelling, representation learning, source separation, and transcription among several others. The emergence of large-scale music foundation models (e.g., MusicLM, Suno, Riffusion) and multimodal systems combining modalities such as text, audio and video has further expanded the creative possibilities of AI-driven music. However, despite these technical advances, there remains a significant gap between the capabilities of current AI music systems and their practical adoption by musicians, composers, producers, and educators. At the same time, there has been a growing consensus in the community that recognizes the limitations of fully autonomous generation and the need for interactive, interpretable, controllable, and human-centred approaches. In light of this emerging trend to maintain meaningful human agency over the creative process, the editors of this special issue organized the first workshop on Emerging AI Technologies for Music, as part of the Association for the Advancement of Artificial Intelligence (AAAI) conference in 2026 at Singapore.

Full proceedings: https://proceedings.mlr.press/v303/},
	author = {K. Bhandari and A. Roy and S. Colton and D. Herremans}
}
