# Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset

arXiv: https://arxiv.org/abs/2004.00188 · HTML: https://mlsanity.com/paper/2004.00188/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 16 of 16)

- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation](https://mlsanity.com/paper/2407.04822/) (2024)
- [The GigaMIDI Dataset with Features for Expressive Music Performance Detection](https://mlsanity.com/paper/2502.17726/) (2025)
- [Learning Interpretable Features in Audio Latent Spaces via Sparse Autoencoders](https://mlsanity.com/paper/2510.23802/) (2025)
- [Toward Deep Drum Source Separation](https://mlsanity.com/paper/2312.09663/) (2023)
- [The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis](https://mlsanity.com/paper/2505.03337/) (2025)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [Text Conditioned Symbolic Drumbeat Generation using Latent Diffusion Models](https://mlsanity.com/paper/2408.02711/) (2024)
- [Analyzing and reducing the synthetic-to-real transfer gap in Music Information Retrieval: the task of automatic drum transcription](https://mlsanity.com/paper/2407.19823/) (2024)
- [Designing Neural Synthesizers for Low-Latency Interaction](https://mlsanity.com/paper/2503.11562/) (2025)
- [Noise-to-Notes: Diffusion-based Generation and Refinement for Automatic Drum Transcription](https://mlsanity.com/paper/2509.21739/) (2025)
- [Learning Linearity in Audio Consistency Autoencoders via Implicit Regularization](https://mlsanity.com/paper/2510.23530/) (2025)
- [Drum Synthesis from Expressive Drum Grids via Neural Audio Codecs](https://mlsanity.com/paper/2605.10281/) (2026)
- [Investigating an Overfitting and Degeneration Phenomenon in Self-Supervised Multi-Pitch Estimation](https://mlsanity.com/paper/2506.23371/) (2025)
- [Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis](https://mlsanity.com/paper/2605.14555/) (2026)
- [Generalized Audio-Driven Synthesis of Precise Drummer Motion](https://mlsanity.com/paper/2608.19055/) (2026)

## Abstract (arXiv)

We introduce the Expanded Groove MIDI dataset (E-GMD), an automatic drum transcription (ADT) dataset that contains 444 hours of audio from 43 drum kits, making it an order of magnitude larger than similar datasets, and the first with human-performed velocity annotations. We use E-GMD to optimize classifiers for use in downstream generation by predicting expressive dynamics (velocity) and show with listening tests that they produce outputs with improved perceptual quality, despite similar results on classification metrics. Via the listening tests, we argue that standard classifier metrics, such as accuracy and F-measure score, are insufficient proxies of performance in downstream tasks because they do not fully align with the perceptual quality of generated outputs.