6 papers · 1 filter
Assessing Factual Music Comprehension in Large Audio Language Models
Daniel Chenyu Lin, Michael Freeman, John Thickstun
Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio. In this paper, we (1) provide empiri…
Music Transcription with (Almost) No Supervision
Saebyeol Shin, Chao Wan, Zhenzhen Liu +4
Competitive music transcription models require large amounts of paired audio-score data, which is scarce due to collection costs, alignment difficulty, and copyright restrictions.…
Robust Neural Audio Fingerprinting using Music Foundation Models
Shubhr Singh, Kiran Bhat, Xavier Riley +3
The proliferation of distorted, compressed, and manipulated music on modern media platforms like TikTok motivates the development of more robust audio fingerprinting techniques to…
Aligning Text-to-Music Evaluation with Human Preferences
Yichen Huang, Zachary Novack, Koichi Saito +5
Despite significant recent advances in generative acoustic text-to-music (TTM) modeling, robust evaluation of these models lags behind, relying in particular on the popular Fréche…
Hookpad Aria: A Copilot for Songwriters
Chris Donahue, Shih-Lun Wu, Yewon Kim +3
We present Hookpad Aria, a generative AI system designed to assist musicians in writing Western pop songs. Our system is seamlessly integrated into Hookpad, a web-based editor desi…
Anticipatory Music Transformer
John Thickstun, David Hall, Chris Donahue +1
We introduce anticipation: a method for constructing a controllable generative model of a temporal point process (the event process) conditioned asynchronously on realizations of a…