5 accepted papers
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 all major music modalities–including sheet music, performance signals, and audio recordings–with multilingual text in a…
Real-time performance tracking is the core component of automatic accompaniment systems. Previous methods typically require full information on the performance score (the human part) and also assume very limited improvisations off the score, otherwise, the tracker will get lost. In this paper, we ai…
Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To bridge this gap, To address this gap, we pr…
We propose a novel method to model hierarchical metrical structures for both symbolic music and audio signals in a self-supervised manner with minimal domain knowledge. The model trains and inferences on beat-aligned music signals and predicts an 8-layer hierarchical metrical tree from beat, measure…
Structure awareness and interpretability are two of the most desired properties of music generation algorithms. Structure-aware models generate more natural and coherent music with long-term dependencies, while interpretable models are more friendly for human-computer interaction and co-creation. To…