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CLC number: TP391.4

On-line Access: 2019-08-05

Received: 2018-02-09

Revision Accepted: 2018-04-13

Crosschecked: 2019-07-12

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Le-kai Zhang

http://orcid.org/0000-0002-8136-5882

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.7 P.964-974

http://doi.org/10.1631/FITEE.1800101


Using psychophysiological measures to recognize personal music emotional experience


Author(s):  Le-kai Zhang, Shou-qian Sun, Bai-xi Xing, Rui-ming Luo, Ke-jun Zhang

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   zlkzhang@zju.edu.cn, ssq@zju.edu.cn, sisyxing@gmail.com, joeluo@zju.edu.cn

Key Words:  Music, Emotion recognition, Physiological signals, Wavelet transform


Le-kai Zhang, Shou-qian Sun, Bai-xi Xing, Rui-ming Luo, Ke-jun Zhang. Using psychophysiological measures to recognize personal music emotional experience[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 964-974.

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year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800101"
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Abstract: 
music can trigger human emotion. This is a psychophysiological process. Therefore, using psychophysiological characteristics could be a way to understand individual music emotional experience. In this study, we explore a new method of personal music emotion recognition based on human physiological characteristics. First, we build up a database of features based on emotions related to music and a database based on physiological signals derived from music listening including EDA, PPG, SKT, RSP, and PD variation information. Then linear regression, ridge regression, support vector machines with three different kernels, decision trees, >k-nearest neighbors, multi-layer perceptron, and Nu support vector regression (NuSVR) are used to recognize music emotions via a data synthesis of music features and human physiological features. NuSVR outperforms the other methods. The correlation coefficient values are 0.7347 for arousal and 0.7902 for valence, while the mean squared errors are 0.023 23 for arousal and 0.014 85 for valence. Finally, we compare the different data sets and find that the data set with all the features (music features and all physiological features) has the best performance in modeling. The correlation coefficient values are 0.6499 for arousal and 0.7735 for valence, while the mean squared errors are 0.029 32 for arousal and 0.015 76 for valence. We provide an effective way to recognize personal music emotional experience, and the study can be applied to personalized music recommendation.

基于心理生理信号的个人音乐情感体验识别

摘要:音乐能激发人的情感,这是一个心理生理过程。因此心理生理特征可用于识别个人音乐情感体验。提出一种新的基于人体生理特征的个人音乐情感识别方法。首先,建立一个基于音乐情感特征的数据库和一个基于听音乐产生的生理信号的数据库,包括心电、脉搏、皮温、呼吸和瞳孔直径变化等生理信号。然后,分别采用线性回归、岭回归、三种不同核的支持向量机、决策树、K近邻算法、多层感知器和Nu支持向量回归(NuSVR)方法,通过音乐特征和人体生理特征识别音乐情感。结果显示,NuSVR性能优于其他方法,其唤醒相关系数为0.7347(均方差为0.023 23),效价相关系数为0.7902(均方差为0.014 85)。最后,对不同数据集进行比较,结果表明所有特征(音乐特征和所有生理特征)数据集在识别中表现最好,唤醒相关系数为0.6499(均方差为0.029 32),效价相关系数为0.7735(均方差为0.015 76)。本文提供了一种有效的个人音乐情感体验识别方法,可用于个性化音乐推荐。

关键词:音乐;情感识别;生理信号;小波变换

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