6 papers
SheetSage2: Coherent Lead-Sheet Transcription with Synthetic Supervision
Junyan Jiang, Ruibin Yuan, Jiahao Pan +4
Transcribing music into a human-readable score requires a coherent understanding of rhythm, harmony, melody, and form. Two obstacles limit this goal: annotated recordings are scarc…
UniVerse: Benchmarking and Enhancing LALMs on Culturally Inclusive Low-Resource Music Understanding
Ziya Zhou, Shangda Wu, Shenyang Xu +16
Recent advances in large audio-language models (LALMs) have significantly improved performance in tasks such as music captioning, genre classification, and sound event detection. H…
DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning
Tristan Wu, Daniel Chin, Junan Zhang +3
Synthesizer inversion is challenging for two main reasons: 1) Distinct parameter configurations can produce perceptually similar sounds. 2) Parameter-space losses often fail to ref…
RenCon 2025: Revival of the Expressive Performance Rendering Competition
Huan Zhang, Taegyun Kwon, Anders Friberg +7
This paper presents a comprehensive documentation of RenCon 2025, the revival of the expressive performance rendering competition which took place at ISMIR 2025 in Daejeon, Korea.…
Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin +2
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality its…
CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages
Shangda Wu, Zhancheng Guo, Ruibin Yuan +7
CLaMP 3 is a unified framework developed to address challenges of cross-modal and cross-lingual generalization in music information retrieval. Using contrastive learning, it aligns…