独奏乐谱图像的乐器分类
Instrument Classification of Solo Sheet Music Images
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中文总结 AI 辅助
本研究利用乐谱图像和预训练语言模型进行乐器分类，通过文本分类方法显著提升准确率，并提出了两种数据增强方法。
中文摘要 AI 辅助
本文研究独奏乐谱的乐器分类问题。以往的工作侧重于音频数据中的乐器识别，而我们则直接利用原始乐谱图像来处理乐器分类问题。我们的方法首先基于bootleg score表示将乐谱图像转换为一系列音乐“单词”，然后将该问题视为文本分类任务。我们证明，通过在未标注数据上训练语言模型，用预训练的语言模型权重初始化分类器，然后在标注数据上微调分类器，可以显著提高分类器的性能。在这项工作中，我们在来自IMSLP的八种不同乐器的独奏乐谱图像上训练了AWD-LSTM、GPT-2和RoBERTa模型。我们发现GPT-2和RoBERTa的表现略优于AWD-LSTM，并且预训练将RoBERTa的分类准确率从34.5%提高到42.9%。此外，我们提出了两种数据增强方法，使RoBERTa的分类准确率额外提高了15%。
英文摘要
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%.
