利用未标记数据改进稀有和弦的分类
Improving the Classification of Rare Chords with Unlabeled Data
摘要：
在这项工作中，我们探索的技术，以提高性能的自动和弦识别（ACR）的任务中罕见的类。我们首先探索了在ACR的背景下使用焦点损失，这最初是为了改善硬样本的分类而提出的。与此同时，我们采用了一种自学习技术，最初设计用于图像识别的音乐领域。我们的实验表明，这两种方法单独（及其组合）提高了罕见和弦的识别，但只使用自学习与噪声添加产生最好的结果。
英文摘要：
In this work, we explore techniques to improve performance for rare classes in the task of Automatic Chord Recognition (ACR). We first explored the use of the focal loss in the context of ACR, which was originally proposed to improve the classification of hard samples. In parallel, we adapted a self-learning technique originally designed for image recognition to the musical domain. Our experiments show that both approaches individually (and their combination) improve the recognition of rare chords, but using only self-learning with noise addition yields the best results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
