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Con- ventional workflows for creating expressive drum ","work_id":"f486bd62-b2e8-4def-a208-a348ed4f9f9d","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis","work_id":"8b9ee716-c084-427f-8765-cb578f3a0d00","ref_index":2,"cited_arxiv_id":"2605.14555","is_internal_anchor":true},{"doi":"","year":null,"title":"1 shows the overview of our proposed method","work_id":"c5c17aa4-ca53-4c29-932d-d831dc3cf80b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2048,"title":"EXPERIMENTS 4.1. Data We train and evaluate our approach on two variations of the Groove MIDI Dataset (GMD)[30], which consists of 1059 unique human- performed MIDI drum sequences aligned with corresp","work_id":"3640e885-d350-4d88-83b9-b344cb0b9578","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"RESULTS our model’s key capabilities are evaluated in this section. 5.1. Temporal Granularity We train our proposed method with drum MIDI representations of different temporal resolutions. 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