# Transcribing Rhythmic Patterns of the Guitar Track in Polyphonic Music

arXiv: https://arxiv.org/abs/2510.05756 · HTML: https://mlsanity.com/paper/2510.05756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [A Foundation Model for Music Informatics](https://mlsanity.com/paper/2311.03318/) (2023)
- [MARBLE: Music Audio Representation Benchmark for Universal Evaluation](https://mlsanity.com/paper/2306.10548/) (2023)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Music2Latent: Consistency Autoencoders for Latent Audio Compression](https://mlsanity.com/paper/2408.06500/) (2024)
- [Beat this! Accurate beat tracking without DBN postprocessing](https://mlsanity.com/paper/2407.21658/) (2024)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [The Sound Demixing Challenge 2023 unicodex2013 Music Demixing Track](https://mlsanity.com/paper/2308.06979/) (2023)
- [A Bi-directional Transformer for Musical Chord Recognition](https://mlsanity.com/paper/1907.02698/) (2019)
- [Modeling Beats and Downbeats with a Time-Frequency Transformer](https://mlsanity.com/paper/2205.14701/) (2022)
- [Feature Learning for Chord Recognition: The Deep Chroma Extractor](https://mlsanity.com/paper/1612.05065/) (2016)
- [A Fully Convolutional Deep Auditory Model for Musical Chord Recognition](https://mlsanity.com/paper/1612.05082/) (2016)
- [SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription](https://mlsanity.com/paper/2309.09085/) (2023)
- [A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems](https://mlsanity.com/paper/2406.18747/) (2024)
- [Improved Chord Recognition by Combining Duration and Harmonic Language Models](https://mlsanity.com/paper/1808.05335/) (2018)
- [Parameter-Efficient Transfer Learning for Music Foundation Models](https://mlsanity.com/paper/2411.19371/) (2024)
- [Scaling Up Music Information Retrieval Training with Semi-Supervised Learning](https://mlsanity.com/paper/2310.01353/) (2023)

## Abstract (arXiv)

Whereas chord transcription has received considerable attention during the past couple of decades, far less work has been devoted to transcribing and encoding the rhythmic patterns that occur in a song. The topic is especially relevant for instruments such as the rhythm guitar, which is typically played by strumming rhythmic patterns that repeat and vary over time. However, in many cases one cannot objectively define a single "right" rhythmic pattern for a given song section. To create a dataset with well-defined ground-truth labels, we asked expert musicians to transcribe the rhythmic patterns in 410 popular songs and record cover versions where the guitar tracks followed those transcriptions. To transcribe the strums and their corresponding rhythmic patterns, we propose a three-step framework. Firstly, we perform approximate stem separation to extract the guitar part from the polyphonic mixture. Secondly, we detect individual strums within the separated guitar audio, using a pre-trained foundation model (MERT) as a backbone. Finally, we carry out a pattern-decoding process in which the transcribed sequence of guitar strums is represented by patterns drawn from an expert-curated vocabulary. We show that it is possible to transcribe the rhythmic patterns of the guitar track in polyphonic music with quite high accuracy, producing a representation that is human-readable and includes automatically detected bar lines and time signature markers. We perform ablation studies and error analysis and propose a set of evaluation metrics to assess the accuracy and readability of the predicted rhythmic pattern sequence.