# Instrument Classification of Solo Sheet Music Images

arXiv: https://arxiv.org/abs/2609.18980 · HTML: https://mlsanity.com/paper/2609.18980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 21 Jul 2026.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [An Attention Mechanism for Musical Instrument Recognition](https://mlsanity.com/paper/1907.04294/) (2019)
- [Deep convolutional neural networks for predominant instrument recognition in polyphonic music](https://mlsanity.com/paper/1605.09507/) (2016)
- [Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining](https://mlsanity.com/paper/2007.14587/) (2020)
- [MIDI Passage Retrieval Using Cell Phone Pictures of Sheet Music](https://mlsanity.com/paper/2004.10347/) (2020)
- [Convolutional Composer Classification](https://mlsanity.com/paper/1911.11737/) (2019)
- [Receptive-Field Regularized CNNs for Music Classification and Tagging](https://mlsanity.com/paper/2007.13503/) (2020)
- [Learning Multi-instrument Classification with Partial Labels](https://mlsanity.com/paper/2001.08864/) (2020)

## Abstract (arXiv)

This paper studies instrument classification of solo sheet music. Whereas previous work has focused on instrument recognition in audio data, we instead approach the instrument classification problem using raw sheet music images. Our approach first converts the sheet music image into a sequence of musical "words" based on the bootleg score representation, and then treats the problem as a text classification task. We show that it is possible to significantly improve classifier performance by training a language model on unlabeled data, initializing a classifier with the pretrained language model weights, and then finetuning the classifier on labeled data. In this work, we train AWD-LSTM, GPT-2, and RoBERTa models on solo sheet music images from IMSLP for eight different instruments. We find that GPT-2 and RoBERTa slightly outperform AWD-LSTM, and that pretraining increases classification accuracy for RoBERTa from 34.5% to 42.9%. Furthermore, we propose two data augmentation methods that increase classification accuracy for RoBERTa by an additional 15%.