8 papers
Decomposer: Learning to Decompile Symbolic Music to Programs
Yewon Kim, Apurva Gandhi, David Chung +2
Musical performance involves executing a set of high-level musical instructions, yet recovering those instructions from the performance is a challenging inverse problem. We present…
What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music
Luyang Zhang, Xirui Jiang, Junwei Deng +3
Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings. This rais…
Computational Copyright: Towards A Royalty Model for Music Generative AI
Junwei Deng, Xirui Jiang, Shiyuan Zhang +5
The rapid rise of generative AI has intensified copyright and economic tensions in creative industries, particularly in music. Current approaches addressing this challenge often fo…
MIDI-LLM: Improving Text-to-MIDI Music Generation via Adapting Large Language Models
Shih-Lun Wu, Yoon Kim, Dave Carlton +3
We present MIDI-LLM, a recipe that improves multitrack text-to-MIDI generation via adapting Large Language Models (LLMs). MIDI-LLM expands an LLM's text vocabulary to include MIDI…
Osu2MIR: Beat Tracking Dataset Derived From Osu! Data
Ziyun Liu, Chris Donahue
In this work, we explore the use of Osu!, a community-based rhythm game, as an alternative source of beat and downbeat annotations. Osu! beatmaps are created and refined by a large…
Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations
Yewon Kim, Sung-Ju Lee, Chris Donahue
Songwriting is often driven by multimodal inspirations, such as imagery, narratives, or existing music, yet songwriters remain unsupported by current music AI systems in incorporat…