# A Hybrid Approach to Audio-to-Score Alignment

arXiv: https://arxiv.org/abs/2007.14333 · HTML: https://mlsanity.com/paper/2007.14333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

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

- [Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks](https://mlsanity.com/paper/2102.00382/) (2021)
- [A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization](https://mlsanity.com/paper/2204.08822/) (2022)

## Abstract (arXiv)

Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time Warping (DTW) and employ handcrafted features. We explore the usage of neural networks as a preprocessing step for DTW-based automatic alignment methods. Experiments on music data from different acoustic conditions demonstrate that this method generates robust alignments whilst being adaptable at the same time.