SOURCE-LINKED INTELLIGENCE

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Automatic Music Transcription (AMT) for guitar remains limited by three challenges: existing systems often fail to capture expressive techniques such as slides, bends, and percussive hits; they often assign notes to incorrect string-fret combinations; and they are typically trained on clean recordings, limiting their generalization to noisy real-world audio. To address these challenges, we propose TART, a modular four-stage audio-to-tablature pipeline consisting of (1) an audio-to-MIDI transcription model, (2) an expressive technique classifier, (3) an audio-conditioned T5 encoder-decoder for

Read original source ↗ Open in workspace

recordType

paper

region

Global