End-to-end Piano Performance-MIDI to Score Conversion with Transformers
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909331352453120 |
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| author | Beyer, Tim Dai, Angela |
| author_facet | Beyer, Tim Dai, Angela |
| contents | The automated creation of accurate musical notation from an expressive human performance is a fundamental task in computational musicology. To this end, we present an end-to-end deep learning approach that constructs detailed musical scores directly from real-world piano performance-MIDI files. We introduce a modern transformer-based architecture with a novel tokenized representation for symbolic music data. Framing the task as sequence-to-sequence translation rather than note-wise classification reduces alignment requirements and annotation costs, while allowing the prediction of more concise and accurate notation. To serialize symbolic music data, we design a custom tokenization stage based on compound tokens that carefully quantizes continuous values. This technique preserves more score information while reducing sequence lengths by $3.5\times$ compared to prior approaches. Using the transformer backbone, our method demonstrates better understanding of note values, rhythmic structure, and details such as staff assignment. When evaluated end-to-end using transcription metrics such as MUSTER, we achieve significant improvements over previous deep learning approaches and complex HMM-based state-of-the-art pipelines. Our method is also the first to directly predict notational details like trill marks or stem direction from performance data. Code and models are available at https://github.com/TimFelixBeyer/MIDI2ScoreTransformer |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00210 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | End-to-end Piano Performance-MIDI to Score Conversion with Transformers Beyer, Tim Dai, Angela Sound Machine Learning Audio and Speech Processing The automated creation of accurate musical notation from an expressive human performance is a fundamental task in computational musicology. To this end, we present an end-to-end deep learning approach that constructs detailed musical scores directly from real-world piano performance-MIDI files. We introduce a modern transformer-based architecture with a novel tokenized representation for symbolic music data. Framing the task as sequence-to-sequence translation rather than note-wise classification reduces alignment requirements and annotation costs, while allowing the prediction of more concise and accurate notation. To serialize symbolic music data, we design a custom tokenization stage based on compound tokens that carefully quantizes continuous values. This technique preserves more score information while reducing sequence lengths by $3.5\times$ compared to prior approaches. Using the transformer backbone, our method demonstrates better understanding of note values, rhythmic structure, and details such as staff assignment. When evaluated end-to-end using transcription metrics such as MUSTER, we achieve significant improvements over previous deep learning approaches and complex HMM-based state-of-the-art pipelines. Our method is also the first to directly predict notational details like trill marks or stem direction from performance data. Code and models are available at https://github.com/TimFelixBeyer/MIDI2ScoreTransformer |
| title | End-to-end Piano Performance-MIDI to Score Conversion with Transformers |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2410.00210 |