Beat-Based Rhythm Quantization of MIDI Performances
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915465447604224 |
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| author | Wachter, Maximilian Murgul, Sebastian Heizmann, Michael |
| author_facet | Wachter, Maximilian Murgul, Sebastian Heizmann, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beat-Based Rhythm Quantization of MIDI Performances Wachter, Maximilian Murgul, Sebastian Heizmann, Michael Sound Computation and Language Multimedia Audio and Speech Processing 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. |
| title | Beat-Based Rhythm Quantization of MIDI Performances |
| topic | Sound Computation and Language Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.19262 |