| Autor: |
Daniel Yang, Kevin Ji, TJ Tsai |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 11, Iss 4, p 1387 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app11041387 |
| Popis: |
This article studies a composer style classification task based on raw sheet music images. While previous works on composer recognition have relied exclusively on supervised learning, we explore the use of self-supervised pretraining methods that have been recently developed for natural language processing. We first convert sheet music images to sequences of musical words, train a language model on a large set of unlabeled musical “sentences”, initialize a classifier with the pretrained language model weights, and then finetune the classifier on a small set of labeled data. We conduct extensive experiments on International Music Score Library Project (IMSLP) piano data using a range of modern language model architectures. We show that pretraining substantially improves classification performance and that Transformer-based architectures perform best. We also introduce two data augmentation strategies and present evidence that the model learns generalizable and semantically meaningful information. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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