A Machine Learning Approach for MIDI to Guitar Tablature Conversion

• URL: http://arxiv.org/abs/2510.10619v1
• Date: Sun, 12 Oct 2025 14:01:01 GMT
• Title: A Machine Learning Approach for MIDI to Guitar Tablature Conversion
• Authors: Maximos Kaliakatsos-Papakostas, Gregoris Bastas, Dimos Makris, Dorien Herremans, Vassilis Katsouros, Petros Maragos,
• Abstract summary: This paper presents a method for assigning guitar tablature notation to a given MIDI-based musical part.<n>The strategy is based on machine learning and requires a basic assumption about how much fingers can stretch on a fretboard.<n>The proposed method also examines the transcription of music pieces that was not meant to be played or could not possibly be played by a guitar.
• Score: 21.416973100105633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Guitar tablature transcription consists in deducing the string and the fret number on which each note should be played to reproduce the actual musical part. This assignment should lead to playable string-fret combinations throughout the entire track and, in general, preserve parsimonious motion between successive combinations. Throughout the history of guitar playing, specific chord fingerings have been developed across different musical styles that facilitate common idiomatic voicing combinations and motion between them. This paper presents a method for assigning guitar tablature notation to a given MIDI-based musical part (possibly consisting of multiple polyphonic tracks), i.e. no information about guitar-idiomatic expressional characteristics is involved (e.g. bending etc.) The current strategy is based on machine learning and requires a basic assumption about how much fingers can stretch on a fretboard; only standard 6-string guitar tuning is examined. The proposed method also examines the transcription of music pieces that was not meant to be played or could not possibly be played by a guitar (e.g. potentially a symphonic orchestra part), employing a rudimentary method for augmenting musical information and training/testing the system with artificial data. The results present interesting aspects about what the system can achieve when trained on the initial and augmented dataset, showing that the training with augmented data improves the performance even in simple, e.g. monophonic, cases. Results also indicate weaknesses and lead to useful conclusions about possible improvements.

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