Just Label The Repeats For In-the-wild Audio-to-score Alignment

Abstract

We propose an efficient workflow for high-quality offline alignment of in-the-wild performance audio and corresponding sheet music scans (images). Recent work on audio-to-score alignment extends dynamic time warping (DTW) to be theoretically able to handle jumps in sheet music induced by repeat signs-this method requires no human annotations, but we show that it often yields low-quality alignments. As an alternative, we propose a workflow and interface that allows users to quickly annotate jumps (by clicking on repeat signs), requiring a small amount of human supervision but yielding much higher quality alignments on average. Additionally, we refine audio and score feature representations to improve alignment quality by: (1) integrating measure detection into the score feature representation, and (2) using raw onset prediction probabilities from a music transcription model instead of piano roll. We propose an evaluation protocol for audio-to-score alignment that computes the distance b

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keybukey2024just

Related papers

• Audio-to-score Alignment Of Piano Music Using Rnn-based Automatic Music Transcription (2017)0.00
• Audio-to-score Alignment Using Deep Automatic Music Transcription (2021)0.00
• Audio-to-score Alignment Using Transposition-invariant Features (2018)0.00
• Learning Frame Similarity Using Siamese Networks For Audio-to-score Alignment (2020)8.09
• A Convolutional-attentional Neural Framework For Structure-aware Performance-score Synchronization (2022)6.34
• Note-level Singing Melody Transcription For Time-aligned Musical Score Generation (2025)5.24
• Multi-modal Conditional Bounding Box Regression For Music Score Following (2021)5.24
• Structure-aware Audio-to-score Alignment Using Progressively Dilated Convolutional Neural Networks (2021)5.84