# A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions

arXiv: https://arxiv.org/abs/2202.12257 · HTML: https://mlsanity.com/paper/2202.12257/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 71 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [Hierarchical Clustering: Objective Functions and Algorithms](https://mlsanity.com/paper/1704.02147/) (2017)
- [Multimodal music information processing and retrieval: survey and future challenges](https://mlsanity.com/paper/1902.05347/) (2019)
- [Audio-to-Score Alignment Using Deep Automatic Music Transcription](https://mlsanity.com/paper/2107.12854/) (2021)
- [Audio-to-score alignment of piano music using RNN-based automatic music transcription](https://mlsanity.com/paper/1711.04480/) (2017)
- [ASMD: an automatic framework for compiling multimodal datasets with audio and scores](https://mlsanity.com/paper/2003.01958/) (2020)

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

- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)
- [Acoustics-specific Piano Velocity Estimation](https://mlsanity.com/paper/2203.16294/) (2022)
- [PianoCoRe: Combined and Refined Piano MIDI Dataset](https://mlsanity.com/paper/2605.06627/) (2026)
- [Transfer of knowledge among instruments in automatic music transcription](https://mlsanity.com/paper/2305.00426/) (2023)

## Abstract (arXiv)

This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change. We propose to distinguish the concept of "performance" from the one of "interpretation", which expresses the "artistic intention". Towards assessing this distinction, we carried out an experimental evaluation where 91 subjects were invited to listen to various audio recordings created by resynthesizing MIDI data obtained through Automatic Music Transcription (AMT) systems and a sensorized acoustic piano. During the resynthesis, we simulated different contexts and asked listeners to evaluate how much the interpretation changes when the context changes. Results show that: (1) MIDI format alone is not able to completely grasp the artistic intention of a music performance; (2) usual objective evaluation measures based on MIDI data present low correlations with the average subjective evaluation. To bridge this gap, we propose a novel measure which is meaningfully correlated with the outcome of the tests. In addition, we investigate multimodal machine learning by providing a new score-informed AMT method and propose an approximation algorithm for the p-dispersion problem.