Fretboardflow: A Dual-Model Approach to Optimize Chord Voicings on the Guitar Fretboard

Smoothly transitioning between chords on the guitar can be a major challenge for beginners, especially when they are only exposed to the most common or single chord diagrams. Yet many chords can be played in multiple ways (i.e., voicings), which can facilitate more comfortable hand movements on the fretboard. To address this, we present the FretboardFlow dataset, featuring 97 songs recorded with a hexaphonic pickup to capture multiple chord voicings as performed by expert guitarists. Our dataset builds upon the GuitarSet processing pipeline, incorporating a Python translation of Prätzlich et al's KAMIR algorithm for interference reduction, for automated hexaphonic transcriptions. Thereby not only capturing harmonic structure but also tacit muscle memory, providing a rich resource for analyzing real-world chord transitions. To predict the most convenient chord voicing within progressions, we propose a dual-model approach integrating both chord and voicing history, and a novel loss function well-suited to the flexible nature of voicings. Our research expands on prior chord prediction work by incorporating expert-recorded voicing variations of the same progressions and introducing a novel machine learning approach to fretboard navigation. We publicly release this dataset as a living resource to support data-driven exploration of personalized guitar instruction.