# Rethinking Evaluation Methodology for Audio-to-Score Alignment

arXiv: https://arxiv.org/abs/2009.14374 · HTML: https://mlsanity.com/paper/2009.14374/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths](https://mlsanity.com/paper/2303.10778/) (2023)
- [Just Label the Repeats for In-The-Wild Audio-to-Score Alignment](https://mlsanity.com/paper/2411.07428/) (2024)

## Abstract (arXiv)

This paper offers a precise, formal definition of an audio-to-score alignment. While the concept of an alignment is intuitively grasped, this precision affords us new insight into the evaluation of audio-to-score alignment algorithms. Motivated by these insights, we introduce new evaluation metrics for audio-to-score alignment. Using an alignment evaluation dataset derived from pairs of KernScores and MAESTRO performances, we study the behavior of our new metrics and the standard metrics on several classical alignment algorithms.