基于用户评论的自动化音乐分类方法  

Automatic Music Classification Method Based on Users' Comments

作　　者：郝建林 黄章进[2] 顾乃杰 

机构地区：[1]中国科学技术大学计算机科学与技术学院,合肥230027 [2]中国科学技术大学安徽省计算与通信重点实验室,合肥230027 [3]中国科学技术大学先进技术研究院,合肥230027

出　　处：《计算机系统应用》2018年第1期154-161,共8页Computer Systems & Applications

基　　金：安徽省自然科学基金(1408085MKL06);高等学校学科创新引智计划项目(B07033)

摘　　要：针对现有音乐平台分类类别固定、检索内容限制过多的问题,本文提出了一种基于用户评论的自动化音乐分类方法.首先,通过linear CRF统计分词模型、n元取词和紧密度分析方法学习得到适合音乐语料分词的字典.其次,使用linear CRF在上述字典的基础上进行分词,对分词结果进行分合测试,修正分词结果.然后,使用优化后的TFIDF关键词提取算法进行标签提取,再经过标签合并得到音乐的候选标签.接着,从全局角度出发对标签进一步筛选,得到音乐的关联标签.最后,建立音乐和标签之间的概率分类模型,对音乐进行分类.实验结果表明,该音乐分类方法准确率较高,可以从用户评论中自动地获取音乐多个维度的分类标签,为个性化的音乐检索提供了保障.An automatic music classification method based on users＇ comments is presented in view of the few categories and limited search content for the existing music platforms. First of all, a linear CRF statistic segmentation model, n-gram word extraction and affinity analysis method are used to obtain a dictionary which can be adapted to music corpus segmentation. Secondly, we use linear CRY model to segment comments with dictiottary above, and then we correct the segmentation result via split-merge testing. Thirdly, the optimized TFIDF keyword extraction model is applied to extract candidate tags, and we merge tags after that. Fourthly, candidate tags with fewer frequency are filtered from a global perspective. Finally, a probability classification network is established between the music and filtered tags to classify music. As the result shows, our music classification method achieves high accuracy. Furthermore, it can ensure the personality of music retrieval for generating music tags automatically in multiple dimensions according to users＇ comments.

关 键 词：音乐分类 分词模型 紧密度分析 关键词提取 关联标签 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]