Novel Music Genre Classification System Using Transfer Learning on a Small Dataset

Yang Wang, T. Goto, Tadaaki Kirisima, K. Tsuchida
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Abstract

Music is an essential "element" in our lives. With the rapid development of technology, the use of compact disks and tapes for listening various forms of music of different cultures has become obsolete. Because of the generation of considerable music data, the accurate classification of music data has become a critical topic of research, and the development of deep learning technology provides a solution for music classification. Transfer learning techniques are widely used in image recognition but are rarely used for music classification. In this study, a small music dataset is used for music classification using transfer learning.
基于小数据集迁移学习的新型音乐类型分类系统
音乐是我们生活中必不可少的“元素”。随着科技的迅速发展,用光盘和磁带来听不同文化的各种形式的音乐已经过时了。由于产生了大量的音乐数据,对音乐数据进行准确的分类已经成为一个重要的研究课题,而深度学习技术的发展为音乐分类提供了解决方案。迁移学习技术广泛应用于图像识别,但很少用于音乐分类。在这项研究中,一个小的音乐数据集被用于使用迁移学习的音乐分类。
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