I. Introduction
Listening to music is a common recreational activity in people's daily life. With the development of communication and network storage technology, large amounts of music are accessible to the general public. To better cater to listeners' preference, a new trend of effectively and efficiently retrieving music according to personal music interests is generated. One solution is to classify and assign labels to music based on genres, mood, artist, etc. [1]. In the early stage, the work of music classification is done manually. However, manual classification can be wrong and cannot provide large scale recommendation [2]. Now music companies are trying to solve this problem using efficient machine learning models. On the one hand, it can reduce the cost of managing and maintaining digital music collections because thousands of music needs to be uploaded to music libraries every year. On the other hand, the classification model can help users find music they are interested in and provide effective recommendation service.