2020/07/29 by T. J. Tsai, Tsai, TJ, Kevin Ji +1
Arts and Humanities · Computer Science · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Diverse Musicological Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing
paper · pdf · doi:10.48550/arxiv.2007.14587
openalex publication_date 2020/07/29 · openalex created_date 2020/08/03 · openalex updated_date 2026/07/28
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.