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

arXiv: https://arxiv.org/abs/2609.11904 · HTML: https://mlsanity.com/paper/2609.11904/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Builds on (most cited first, 17 of 17)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation](https://mlsanity.com/paper/2203.09893/) (2022)
- [Unaligned Supervision For Automatic Music Transcription in The Wild](https://mlsanity.com/paper/2204.13668/) (2022)
- [DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models](https://mlsanity.com/paper/2107.14653/) (2021)
- [High Resolution Guitar Transcription via Domain Adaptation](https://mlsanity.com/paper/2402.15258/) (2024)
- [BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking](https://mlsanity.com/paper/2108.03576/) (2021)
- [towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model](https://mlsanity.com/paper/2202.09907/) (2022)
- [GAPS: A Large and Diverse Classical Guitar Dataset and Benchmark Transcription Model](https://mlsanity.com/paper/2408.08653/) (2024)
- [SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription](https://mlsanity.com/paper/2309.09085/) (2023)
- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)
- [MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling](https://mlsanity.com/paper/2408.05024/) (2024)
- [Guitar-TECHS: An Electric Guitar Dataset Covering Techniques, Musical Excerpts, Chords and Scales Using a Diverse Array of Hardware](https://mlsanity.com/paper/2501.03720/) (2025)
- [Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription](https://mlsanity.com/paper/2506.14223/) (2025)

## Abstract (arXiv)

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 string-fret assignment, and (4) an automated tablature generator. We evaluate TART in a zero-shot setting on GuitarSet, EGDB, and two augmented benchmarks, Noisy GuitarSet and Noisy EGDB. Averaged across these four benchmarks, TART achieves 81.35% audio-to-MIDI F50 (+6.67 points over the best prior baseline), 71.8% string-fret Tab F1 (+8.5 points over the best prior baseline), and 54.08% end-to-end Tab F1. To our knowledge, TART is the first framework to generate guitar tablature with both fingering and expressive technique annotations directly from guitar audio.