dorsal/arxiv
View SchemaTowards Realistic Synthetic Data for Automatic Drum Transcription
| Authors | Pierfrancesco Melucci, Paolo Merialdo, Taketo Akama |
|---|---|
| Categories | |
| ArXiv ID | 2601.09520vv1 |
| URL | https://arxiv.org/abs/2601.09520 |
| License | http://creativecommons.org/licenses/by-sa/4.0/ |
Abstract
Deep learning models define the state-of-the-art in Automatic Drum Transcription (ADT), yet their performance is contingent upon large-scale, paired audio-MIDI datasets, which are scarce. Existing workarounds that use synthetic data often introduce a significant domain gap, as they typically rely on low-fidelity SoundFont libraries that lack acoustic diversity. While high-quality one-shot samples offer a better alternative, they are not available in a standardized, large-scale format suitable for training. This paper introduces a new paradigm for ADT that circumvents the need for paired audio-MIDI training data. Our primary contribution is a semi-supervised method to automatically curate a large and diverse corpus of one-shot drum samples from unlabeled audio sources. We then use this corpus to synthesize a high-quality dataset from MIDI files alone, which we use to train a sequence-to-sequence transcription model. We evaluate our model on the ENST and MDB test sets, where it achieves new state-of-the-art results, significantly outperforming both fully supervised methods and previous synthetic-data approaches. The code for reproducing our experiments is publicly available at https://github.com/pier-maker92/ADT_STR
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"abstract": "Deep learning models define the state-of-the-art in Automatic Drum Transcription (ADT), yet their performance is contingent upon large-scale, paired audio-MIDI datasets, which are scarce. Existing workarounds that use synthetic data often introduce a significant domain gap, as they typically rely on low-fidelity SoundFont libraries that lack acoustic diversity. While high-quality one-shot samples offer a better alternative, they are not available in a standardized, large-scale format suitable for training. This paper introduces a new paradigm for ADT that circumvents the need for paired audio-MIDI training data. Our primary contribution is a semi-supervised method to automatically curate a large and diverse corpus of one-shot drum samples from unlabeled audio sources. We then use this corpus to synthesize a high-quality dataset from MIDI files alone, which we use to train a sequence-to-sequence transcription model. We evaluate our model on the ENST and MDB test sets, where it achieves new state-of-the-art results, significantly outperforming both fully supervised methods and previous synthetic-data approaches. The code for reproducing our experiments is publicly available at https://github.com/pier-maker92/ADT_STR",
"arxiv_id": "2601.09520",
"authors": [
"Pierfrancesco Melucci",
"Paolo Merialdo",
"Taketo Akama"
],
"categories": [
"cs.SD",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-sa/4.0/",
"title": "Towards Realistic Synthetic Data for Automatic Drum Transcription",
"url": "https://arxiv.org/abs/2601.09520",
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