论文标题
通过未标记的数据改善稀有和弦的分类
Improving the Classification of Rare Chords with Unlabeled Data
论文作者
论文摘要
在这项工作中，我们探讨了在自动和弦识别任务（ACR）任务中提高稀有类别的性能的技术。我们首先探讨了在ACR背景下使用焦点损失的使用，该局部损失最初是为了改善硬样品的分类而提出的。同时，我们改编了一种最初是为图像识别到音乐领域而设计的自学习技术。我们的实验表明，这两种方法均单独（及其组合）提高了对稀有和弦的识别，但是仅使用添加噪声的自学习可以产生最佳结果。
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.
