# Quantifying the Corpus Bias Problem in Automatic Music Transcription Systems

arXiv: https://arxiv.org/abs/2408.04737 · HTML: https://mlsanity.com/paper/2408.04737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [Sequence-to-Sequence Piano Transcription with Transformers](https://mlsanity.com/paper/2107.09142/) (2021)
- [Computer-Generated Music for Tabletop Role-Playing Games](https://mlsanity.com/paper/2008.07009/) (2020)
- [Automatic Piano Transcription with Hierarchical Frequency-Time Transformer](https://mlsanity.com/paper/2307.04305/) (2023)
- [On the Potential of Simple Framewise Approaches to Piano Transcription](https://mlsanity.com/paper/1612.05153/) (2016)
- [A Data-Driven Analysis of Robust Automatic Piano Transcription](https://mlsanity.com/paper/2402.01424/) (2024)
- [Polyphonic Piano Transcription Using Autoregressive Multi-State Note Model](https://mlsanity.com/paper/2010.01104/) (2020)
- [Towards Musically Informed Evaluation of Piano Transcription Models](https://mlsanity.com/paper/2406.08454/) (2024)
- [An Experimental Analysis of the Entanglement Problem in Neural-Network-based Music Transcription Systems](https://mlsanity.com/paper/1702.00025/) (2017)

## Built on by (most cited first, 3 of 3)

- [Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling](https://mlsanity.com/paper/2504.15071/) (2025)
- [Pay Attention to the Keys: Visual Piano Transcription Using Transformers](https://mlsanity.com/paper/2411.09037/) (2024)
- [Multi-Task Multi-Frame Visual Piano Transcription](https://mlsanity.com/paper/2608.03419/) (2026)

## Abstract (arXiv)

Automatic Music Transcription (AMT) is the task of recognizing notes in audio recordings of music. The State-of-the-Art (SotA) benchmarks have been dominated by deep learning systems. Due to the scarcity of high quality data, they are usually trained and evaluated exclusively or predominantly on classical piano music. Unfortunately, that hinders our ability to understand how they generalize to other music. Previous works have revealed several aspects of memorization and overfitting in these systems. We identify two primary sources of distribution shift: the music, and the sound. Complementing recent results on the sound axis (i.e. acoustics, timbre), we investigate the musical one (i.e. note combinations, dynamics, genre). We evaluate the performance of several SotA AMT systems on two new experimental test sets which we carefully construct to emulate different levels of musical distribution shift. Our results reveal a stark performance gap, shedding further light on the Corpus Bias problem, and the extent to which it continues to trouble these systems.