一种基于Web挖掘的音乐流派分类方法

A Web Mining based Method for Music Genre Classification

本文提出一种基于web挖掘的音乐流派分类方法,以Last.fm2音乐网站的用户标签为特征进行音乐艺术家的相似性比较,并依据艺术家间的相似度进行流派分类。其中,艺术家间的相似度通过计算标签的共现(co-occurrence)获得,音乐流派分类使用k最近邻(k-NN)方法。实验表明,使用音乐标签对音乐艺术家进行流派分类,能获得较高的准确率,本文提出的方法优于文献提出的基于web挖掘的音乐流派分类方法,将分类准确率由89.5%提高到95%。

In this paper, we present a music genre classification method based on web mining. We propose a similarity calculation and genre classification measure for music artists with the use-defined tags from Last.fm. Similarities between artists are calculated based on tag co-occurrence. The k-nearest neighbor algorithm （k-NN） has been used to classify the music genre. Experiments show that tags are effective to characterize similarities between artists and the proposed approach outperforms the previous web mining approaches in artist genre classification with the average accuracy of 95%, compared with 89.5% of.