# Audio-to-Score Alignment Using Deep Automatic Music Transcription

arXiv: https://arxiv.org/abs/2107.12854 · HTML: https://mlsanity.com/paper/2107.12854/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2021.

## Built on by (most cited first, 7 of 7)

- [A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions](https://mlsanity.com/paper/2202.12257/) (2022)
- [ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio](https://mlsanity.com/paper/2408.00674/) (2024)
- [Optical Music Recognition in Manuscripts from the Ricordi Archive](https://mlsanity.com/paper/2408.10260/) (2024)
- [Acoustics-specific Piano Velocity Estimation](https://mlsanity.com/paper/2203.16294/) (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)
- [RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection](https://mlsanity.com/paper/2507.12175/) (2025)

## Abstract (arXiv)

Audio-to-score alignment (A2SA) is a multimodal task consisting in the alignment of audio signals to music scores. Recent literature confirms the benefits of Automatic Music Transcription (AMT) for A2SA at the frame-level. In this work, we aim to elaborate on the exploitation of AMT Deep Learning (DL) models for achieving alignment at the note-level. We propose a method which benefits from HMM-based score-to-score alignment and AMT, showing a remarkable advancement beyond the state-of-the-art. We design a systematic procedure to take advantage of large datasets which do not offer an aligned score. Finally, we perform a thorough comparison and extensive tests on multiple datasets.