混合F-MFCC参数与多项集成ML算法的音乐情感分类方法研究  

Music Sentiment Classification Method Based on Hybrid F-MFCC Parameters and Multi integrated ML Algorithm

作　　者：刘丹霞 李西萍 路惠捷 Liu Danxia;Li Xiping;Liu Siqi(College of Basic Medical Sciences,Air Force Medical University,Xian,710032,China;Teaching and Research Support Center,Air Force Medical University,Xian,710032,China;Department of Military Psychology,Air Force Medical University,Xian,710032,China)

机构地区：[1]空军军医大学基础医学院,陕西西安710032 [2]空军军医大学教研保障中心,陕西西安710032 [3]空军军医大学军事医学心理学系,陕西西安710032

出　　处：《现代科学仪器》2024年第6期369-374,共6页Modern Scientific Instruments

基　　金：2022年度国防军事教育科研项目军队重点课题“军队院校思政课程与课程思政协同育人问题研究”,项目编号:JYKY-C2022032。

摘　　要：针对目前音乐情感分类方法存在的特征提取不充分、准确率不高的问题,研究提出了一种改进梅尔频率倒谱系数,以更好提取地音乐情感特征,并结合多项集成机器算法来对音乐情感进行分类。结果表明,改进后的梅尔频率倒谱系数参数对愤怒、高兴、放松、悲伤4种情感特征的提取准确率分别为72.5%、66.9%、58.2%、56.3%。研究方法对四种情感的整体分类准确率均高于对比算法,分别达到了90.3%、89.6%、91.4%、92.5%。实验结果显示,通过结合改进的梅尔频率倒谱系数参数和多项集成机器学习算法,显著提高了音乐情感分类的准确率,为智能音乐推荐和情感分析提供了高效的技术支持。Aiming at the problems of insufficient feature extraction and low accuracy in current music emotion classification methods,a study proposes an improved Mel frequency cepstral coefficient to better extract music emotion features,and combines multiple integrated machine algorithms to classify music emotions.The results showed that the improved Mel frequency cepstral coefficient parameters had extraction accuracies of 72.5%,66.9%,58.2%,and 56.3%for the four emotional features of anger,happiness,relaxation,and sadness,respectively.The overall classification accuracy of the research method for the four emotions is higher than that of the comparison algorithm,reaching 90.3%,89.6%,91.4%,and 92.5%,respectively.The experimental results show that by combining the improved Mel frequency cepstral coefficient parameters and multiple integrated machine learning algorithms,the accuracy of music sentiment classification has been significantly improved,providing efficient technical support for intelligent music recommendation and sentiment analysis.

关 键 词：F-MFCC ML 音乐情感分类 特征提取 多头注意力机制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]