# Joint Transcription of Acoustic Guitar Strumming Directions and Chords

arXiv: https://arxiv.org/abs/2508.07973 · HTML: https://mlsanity.com/paper/2508.07973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [A Bi-directional Transformer for Musical Chord Recognition](https://mlsanity.com/paper/1907.02698/) (2019)
- [High Resolution Guitar Transcription via Domain Adaptation](https://mlsanity.com/paper/2402.15258/) (2024)
- [YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation](https://mlsanity.com/paper/2407.04822/) (2024)
- [DawDreamer: Bridging the Gap Between Digital Audio Workstations and Python Interfaces](https://mlsanity.com/paper/2111.09931/) (2021)
- [Feature Learning for Chord Recognition: The Deep Chroma Extractor](https://mlsanity.com/paper/1612.05065/) (2016)

## Abstract (arXiv)

Automatic transcription of guitar strumming is an underrepresented and challenging task in Music Information Retrieval (MIR), particularly for extracting both strumming directions and chord progressions from audio signals. While existing methods show promise, their effectiveness is often hindered by limited datasets. In this work, we extend a multimodal approach to guitar strumming transcription by introducing a novel dataset and a deep learning-based transcription model. We collect 90 min of real-world guitar recordings using an ESP32 smartwatch motion sensor and a structured recording protocol, complemented by a synthetic dataset of 4h of labeled strumming audio. A Convolutional Recurrent Neural Network (CRNN) model is trained to detect strumming events, classify their direction, and identify the corresponding chords using only microphone audio. Our evaluation demonstrates significant improvements over baseline onset detection algorithms, with a hybrid method combining synthetic and real-world data achieving the highest accuracy for both strumming action detection and chord classification. These results highlight the potential of deep learning for robust guitar strumming transcription and open new avenues for automatic rhythm guitar analysis.