mlsanityData to 14 Oct 2026

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

arXiv 2004.00188 ↗First posted 1 Apr 2020cs.SD cs.LGcited by 16

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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.

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

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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 4; 2025: 7; 2026: 3.

16 papers.

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