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Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset
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Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset
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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.
Forward citations
Cited by 3 Pith papers
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P-MUSE: Prompt-MIDI-Optional Model for Unified Instrumental Music Synthesis and Editing
P-MUSE unifies MIDI-to-music generation and local editing under one flow-matching model that accepts paired, style, or mixed prompts, and introduces a Tail-Drop guidance schedule that improves quality.
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Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis
Break-the-Beat! renders drum MIDI audio that matches the timbre of a reference clip by fine-tuning a text-to-audio model with a content encoder and hybrid conditioning on a new paired dataset.
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Drum Synthesis from Expressive Drum Grids via Neural Audio Codecs
A Transformer predicts tokens from neural audio codecs (EnCodec, DAC, X-Codec) to convert expressive drum grids into audio, trained and evaluated on the E-GMD dataset using objective metrics.
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