@inproceedings{HambergerMurgulSchmidtetal.2025,
  author    = {Hamberger, Anna and Murgul, Sebastian and Schmidt, Jochen and Heizmann, Michael},
  title     = {Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription},
  booktitle = {Proceedings of the 50th International Computer Music Conference 2025},
  publisher = {The International Computer Music Association},
  institution = {Fakult{\"a}t f{\"u}r Informatik},
  pages     = {438 -- 445},
  year      = {2025},
  abstract  = {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.},
  language  = {en}
}