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

Analysis

Why This Paper Matters

Automatic Music Transcription (AMT) for guitar has long been hindered by three persistent challenges: capturing expressive techniques like slides, bends, and percussive hits; correctly assigning notes to string-fret combinations; and generalizing to noisy, real-world recordings. These limitations have kept guitar tablature generation largely impractical for expressive performances. TART directly addresses these gaps by introducing a modular pipeline that not only transcribes notes but also annotates expressive techniques and fingering, making it a significant step toward fully automated, expressive guitar transcription.

The paper's focus on zero-shot evaluation across multiple benchmarks, including noisy variants, highlights its potential for real-world deployment. By tackling the string-fret assignment problem with an audio-conditioned T5 encoder-decoder, TART moves beyond traditional heuristic or rule-based methods, leveraging modern sequence-to-sequence learning. This work matters because it bridges the gap between raw audio and playable tablature, which is essential for applications in music education, interactive music tools, and computational musicology.

Technical Contributions

• Modular four-stage pipeline: TART decomposes the transcription task into audio-to-MIDI transcription, expressive technique classification, string-fret assignment, and tablature generation, allowing each component to be developed and improved independently.
• Expressive technique classifier: A dedicated model identifies techniques such as slides, bends, and percussive hits, which are often ignored by standard AMT systems.
• Audio-conditioned T5 encoder-decoder: This component assigns notes to correct string-fret combinations by conditioning on the audio, addressing a core challenge in guitar transcription.
• Automated tablature generator: The final stage produces human-readable tablature with both fingering and technique annotations.
• Zero-shot evaluation on noisy benchmarks: The system is tested on GuitarSet, EGDB, and augmented noisy versions, demonstrating robustness without task-specific fine-tuning.

Results

TART achieves strong zero-shot performance across four benchmarks. Averaged results show 81.35% audio-to-MIDI F50, a +6.67 point improvement over the best prior baseline. For string-fret assignment, it reaches 71.8% Tab F1, outperforming the best baseline by +8.5 points. End-to-end Tab F1 is 54.08%, indicating that while the full pipeline is challenging, it substantially advances the state of the art. These gains are consistent across clean and noisy datasets, underscoring the system's generalization ability.

Significance

TART's significance lies in its holistic approach to guitar transcription, integrating expressive technique recognition and accurate fingering assignment. This could enable new applications in music education, where students receive automated feedback on technique and fingering, and in music production, where guitar parts can be transcribed and edited more efficiently. The modular design also facilitates future improvements as individual components advance. By releasing a framework that handles noisy audio and expressive techniques, TART sets a new benchmark for guitar AMT and encourages further research into expressive music transcription.