TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription
TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.
TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.
- Four-stage pipeline: audio-to-MIDI model, technique classifier, audio-conditioned T5, tablature generator.
- Averages 81.35% audio-to-MIDI F50 and 71.8% string-fret Tab F1 across four benchmarks.
- First framework producing tablature with both fingering and expressive technique annotations from audio.
- Evaluated zero-shot on GuitarSet, EGDB, and noisy augmented variants.
From the original article
1 min read at arxiv.orgAutomatic 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.
By Akshaj Gupta · arxiv.org
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