{"ID":2881063,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19262","arxiv_id":"2508.19262","title":"Beat-Based Rhythm Quantization of MIDI Performances","abstract":"We propose a transformer-based rhythm quantization model that incorporates beat and downbeat information to quantize MIDI performances into metrically-aligned, human-readable scores. We propose a beat-based preprocessing method that transfers score and performance data into a unified token representation. We optimize our model architecture and data representation and train on piano and guitar performances. Our model exceeds state-of-the-art performance based on the MUSTER metric.","short_abstract":"We propose a transformer-based rhythm quantization model that incorporates beat and downbeat information to quantize MIDI performances into metrically-aligned, human-readable scores. We propose a beat-based preprocessing method that transfers score and performance data into a unified token representation. We optimize o...","url_abs":"https://arxiv.org/abs/2508.19262","url_pdf":"https://arxiv.org/pdf/2508.19262v1","authors":"[\"Maximilian Wachter\",\"Sebastian Murgul\",\"Michael Heizmann\"]","published":"2025-08-18T10:07:20Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CL\",\"cs.MM\",\"eess.AS\"]","methods":"[\"Transformer\"]","has_code":false}