  This paper studies composer style classification of piano sheet music images.
Previous approaches to the composer classification task have been limited by a
scarcity of data. We address this issue in two ways: (1) we recast the problem
to be based on raw sheet music images rather than a symbolic music format, and
(2) we propose an approach that can be trained on unlabeled data. Our approach
first converts the sheet music image into a sequence of musical "words" based
on the bootleg feature representation, and then feeds the sequence into a text
classifier. We show that it is possible to significantly improve classifier
performance by first training a language model on a set of unlabeled data,
initializing the classifier with the pretrained language model weights, and
then finetuning the classifier on a small amount of labeled data. We train
AWD-LSTM, GPT-2, and RoBERTa language models on all piano sheet music images in
IMSLP. We find that transformer-based architectures outperform CNN and LSTM
models, and pretraining boosts classification accuracy for the GPT-2 model from
46\% to 70\% on a 9-way classification task. The trained model can also be used
as a feature extractor that projects piano sheet music into a feature space
that characterizes compositional style.
