TY - CHAP A1 - Hamberger, Anna A1 - Murgul, Sebastian A1 - Schmidt, Jochen A1 - Heizmann, Michael T1 - Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription T2 - Proceedings of the 50th International Computer Music Conference 2025 N2 - 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. KW - Machine Learning Y1 - 2025 SP - 438 EP - 445 PB - The International Computer Music Association ER -