基于支持向量机与LASSO的双模态音乐分类与评价  

Bimodal Music Classification and Evaluation Based on Support Vector Machine and LASSO

作　　者：周涛 ZHOU Tao(Nanjing University of Information Science and Technology,Nanjing,Jiangsu Province,210044 China)

机构地区：[1]南京信息工程大学,江苏南京210044

出　　处：《科技创新导报》2021年第19期75-78,共4页Science and Technology Innovation Herald

摘　　要：随着互联网技术的高速发展,人们与数字音乐的关系更加紧密,人们会依据自己的偏好以及场所选择音乐,因此如何有效管理数量庞大的音乐并对其分门别类显得尤为重要。为提高音乐分类的准确率,本文从音频中提取特征向量,运用遗传算法优化支持向量机实现音乐流派分类;从歌词中提取特征关键词,采用L A S SO降维实现文本情感分类,最终构建双模态音乐分类模型。结果表明,该分类方法准确率为73.1%,可靠性与稳定性良好,有效地避免了传统方法产生局部最优的问题。With the rapid development of Internet technology,people have a closer relationship with digital music.People will choose music according to their preferences and places.Therefore,how to effectively manage a large number of music and its classification is particularly important.In order to improve the accuracy of music classification,this paper extracts feature vector from audio,uses genetic algorithm to optimize support vector machine to achieve music faction classification;ex-tracts feature keywords from lyrics,uses lasso dimension reduction to achieve text sentiment classification,and finally constructs a dual-mode music classification model.The results show that the accuracy rate of the classification method is 73.1%,the reliability and stability are good,and the problem of local optimum is avoided effectively.

关 键 词：音乐分类 支持向量机 遗传算法 L ASSO 双模态融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]