Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916796727033856 |
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| author | Hamberger, Anna Murgul, Sebastian Schmidt, Jochen Heizmann, Michael |
| author_facet | Hamberger, Anna Murgul, Sebastian Schmidt, Jochen Heizmann, Michael |
| contents | Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_14223 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription Hamberger, Anna Murgul, Sebastian Schmidt, Jochen Heizmann, Michael Sound Computation and Language Multimedia Audio and Speech Processing Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription. |
| title | Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription |
| topic | Sound Computation and Language Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.14223 |