2 research outputs found
Convolutional Composer Classification
This paper investigates end-to-end learnable models for attributing composers
to musical scores. We introduce several pooled, convolutional architectures for
this task and draw connections between our approach and classical learning
approaches based on global and n-gram features. We evaluate models on a corpus
of 2,500 scores from the KernScores collection, authored by a variety of
composers spanning the Renaissance era to the early 20th century. This corpus
has substantial overlap with the corpora used in several previous, smaller
studies; we compare our results on subsets of the corpus to these previous
works.Comment: 8 pages, published at ISMIR 201
Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining
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.Comment: 8 pages, 7 figures. Accepted paper at the International Society for
Music Information Retrieval Conference (ISMIR) 202