# Audio-to-score alignment of piano music using RNN-based automatic music transcription

arXiv: https://arxiv.org/abs/1711.04480 · HTML: https://mlsanity.com/paper/1711.04480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions](https://mlsanity.com/paper/2202.12257/) (2022)
- [Contextualized Token Discrimination for Speech Search Query Correction](https://mlsanity.com/paper/2509.04393/) (2025)
- [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)

We propose a framework for audio-to-score alignment on piano performance that employs automatic music transcription (AMT) using neural networks. Even though the AMT result may contain some errors, the note prediction output can be regarded as a learned feature representation that is directly comparable to MIDI note or chroma representation. To this end, we employ two recurrent neural networks that work as the AMT-based feature extractors to the alignment algorithm. One predicts the presence of 88 notes or 12 chroma in frame-level and the other detects note onsets in 12 chroma. We combine the two types of learned features for the audio-to-score alignment. For comparability, we apply dynamic time warping as an alignment algorithm without any additional post-processing. We evaluate the proposed framework on the MAPS dataset and compare it to previous work. The result shows that the alignment framework with the learned features significantly improves the accuracy, achieving less than 10 ms in mean onset error.