# Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment

arXiv: https://arxiv.org/abs/2011.07546 · HTML: https://mlsanity.com/paper/2011.07546/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [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)
- [Multi-modal Conditional Bounding Box Regression for Music Score Following](https://mlsanity.com/paper/2105.04309/) (2021)
- [Barwise Compression Schemes for Audio-Based Music Structure Analysis](https://mlsanity.com/paper/2202.04981/) (2022)
- [Towards Context-Aware Neural Performance-Score Synchronisation](https://mlsanity.com/paper/2206.00454/) (2022)
- [Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations](https://mlsanity.com/paper/2506.22237/) (2025)

## 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, which cannot be adapted to different acoustic conditions. We propose a method to overcome this limitation using learned frame similarity for audio-to-score alignment. We focus on offline audio-to-score alignment of piano music. Experiments on music data from different acoustic conditions demonstrate that our method achieves higher alignment accuracy than a standard DTW-based method that uses handcrafted features, and generates robust alignments whilst being adaptable to different domains at the same time.