# High Resolution Guitar Transcription via Domain Adaptation

arXiv: https://arxiv.org/abs/2402.15258 · HTML: https://mlsanity.com/paper/2402.15258/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [Learning Features of Music from Scratch](https://mlsanity.com/paper/1611.09827/) (2016)
- [Sequence-to-Sequence Piano Transcription with Transformers](https://mlsanity.com/paper/2107.09142/) (2021)
- [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)
- [Omnizart: A General Toolbox for Automatic Music Transcription](https://mlsanity.com/paper/2106.00497/) (2021)
- [Multitrack Music Transcription with a Time-Frequency Perceiver](https://mlsanity.com/paper/2306.10785/) (2023)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)

## Built on by (most cited first, 15 of 15)

- [YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation](https://mlsanity.com/paper/2407.04822/) (2024)
- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling](https://mlsanity.com/paper/2408.05024/) (2024)
- [Count The Notes: Histogram-Based Supervision for Automatic Music Transcription](https://mlsanity.com/paper/2511.14250/) (2025)
- [Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription](https://mlsanity.com/paper/2506.14223/) (2025)
- [Beat and Downbeat Tracking in Performance MIDI Using an End-to-End Transformer Architecture](https://mlsanity.com/paper/2507.00466/) (2025)
- [VioPTT: Violin Technique-Aware Transcription from Synthetic Data Augmentation](https://mlsanity.com/paper/2509.23759/) (2025)
- [Music Transcription with (Almost) No Supervision](https://mlsanity.com/paper/2605.24193/) (2026)
- [Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations](https://mlsanity.com/paper/2506.22237/) (2025)
- [Joint Transcription of Acoustic Guitar Strumming Directions and Chords](https://mlsanity.com/paper/2508.07973/) (2025)
- [Exploring Procedural Data Generation for Automatic Acoustic Guitar Fingerpicking Transcription](https://mlsanity.com/paper/2508.07987/) (2025)
- [Velocity Prediction in Automatic Guitar Transcription](https://mlsanity.com/paper/2606.24912/) (2026)
- [Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset](https://mlsanity.com/paper/2608.06165/) (2026)
- [Playability-Aware Audio-to-Tablature Guitar Transcription via Diffusion Models](https://mlsanity.com/paper/2608.30854/) (2026)
- [TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription](https://mlsanity.com/paper/2609.11904/) (2026)

## Abstract (arXiv)

Automatic music transcription (AMT) has achieved high accuracy for piano due to the availability of large, high-quality datasets such as MAESTRO and MAPS, but comparable datasets are not yet available for other instruments. In recent work, however, it has been demonstrated that aligning scores to transcription model activations can produce high quality AMT training data for instruments other than piano. Focusing on the guitar, we refine this approach to training on score data using a dataset of commercially available score-audio pairs. We propose the use of a high-resolution piano transcription model to train a new guitar transcription model. The resulting model obtains state-of-the-art transcription results on GuitarSet in a zero-shot context, improving on previously published methods.