# FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription

arXiv: https://arxiv.org/abs/2212.03023 · HTML: https://mlsanity.com/paper/2212.03023/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## 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)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (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)
- [The continuous Bernoulli: fixing a pervasive error in variational autoencoders](https://mlsanity.com/paper/1907.06845/) (2019)
- [A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation](https://mlsanity.com/paper/2203.09893/) (2022)
- [Deep Polyphonic ADSR Piano Note Transcription](https://mlsanity.com/paper/1906.09165/) (2019)
- [towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model](https://mlsanity.com/paper/2202.09907/) (2022)
- [DEEPF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals](https://mlsanity.com/paper/2102.06306/) (2021)
- [A Data-Driven Methodology for Considering Feasibility and Pairwise Likelihood in Deep Learning Based Guitar Tablature Transcription Systems](https://mlsanity.com/paper/2204.08094/) (2022)

## Built on by (most cited first, 5 of 5)

- [High Resolution Guitar Transcription via Domain Adaptation](https://mlsanity.com/paper/2402.15258/) (2024)
- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [GuitarFlow: Realistic Electric Guitar Synthesis From Tablatures via Flow Matching and Style Transfer](https://mlsanity.com/paper/2510.21872/) (2025)
- [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)

In recent years, the task of Automatic Music Transcription (AMT), whereby various attributes of music notes are estimated from audio, has received increasing attention. At the same time, the related task of Multi-Pitch Estimation (MPE) remains a challenging but necessary component of almost all AMT approaches, even if only implicitly. In the context of AMT, pitch information is typically quantized to the nominal pitches of the Western music scale. Even in more general contexts, MPE systems typically produce pitch predictions with some degree of quantization. In certain applications of AMT, such as Guitar Tablature Transcription (GTT), it is more meaningful to estimate continuous-valued pitch contours. Guitar tablature has the capacity to represent various playing techniques, some of which involve pitch modulation. Contemporary approaches to AMT do not adequately address pitch modulation, and offer only less quantization at the expense of more model complexity. In this paper, we present a GTT formulation that estimates continuous-valued pitch contours, grouping them according to their string and fret of origin. We demonstrate that for this task, the proposed method significantly improves the resolution of MPE and simultaneously yields tablature estimation results competitive with baseline models.