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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

[1]Agrafioti F, Hatzinakos D, Anderson AK, 2012. ECG pattern analysis for emotion detection. IEEE Trans Affect Comput, 3(1):102-115.

[2]Ayadi ME, Kamel MS, Karray F, 2011. Survey on speech emotion recognition: features, classification schemes, and databases. Patt Recogn, 44(3):572-587.

[3]Bradley MM, Lang PJ, 1994. Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psych, 25(1):49-59.

[4]Chandler C, Cornes R, 2012. Biometric measurement of human emotions. TECHNIA - Int J Comput Sci Commun Technol, 4(2):164-168.

[5]Cheng B, Liu GY, 2008. Emotion recognition from surface EMG signal using wavelet transform and neural network. J Comput Appl, 28(2):333-335 (in Chinese).

[6]de Witte NAJ, Sütterlin S, Braet C, et al., 2017. Psychophysiological correlates of emotion regulation training in adolescent anxiety: evidence from the novel PIER task. J Affect Disord, 214:89-96.

[7]Gerdes ABM, Wieser MJ, Alpers GW, 2014. Emotional pictures and sounds: a review of multimodal interactions of emotion cues in multiple domains. Front Psychol, 5:1351.

[8]Katsis CD, Katertsidis NS, Fotiadis DI, 2011. An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomed Signal Process Contr, 6(3):261-268.

[9]Kim J, André E, 2008. Emotion recognition based on physiological changes in music listening. IEEE Trans Patt Anal Mach Intell, 30(12):2067-2083.

[10]Koelstra S, Muhl C, Soleymani M, et al., 2012. DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput, 3(1):18-31.

[11]Krumhansl CL, 1997. An exploratory study of musical emotions and psychophysiology. Can J Exp Psychol, 51(4): 336-353.

[12]Li C, Xu C, Feng ZY, 2016. Analysis of physiological for emotion recognition with the IRS model. Neurocomputing, 178:103-111.

[13]Li SS, Walters G, Packer J, et al., 2017. A comparative analysis of self-report and psychophysiological measures of emotion in the context of tourism advertising. J Travel Res, 57(8):1078-1092.

[14]Lu Q, Chen XO, Yang DS, et al., 2010. Boosting for multi-modal music emotion classification. 11^th Int Society for Music Information Retrieval Conf, p.105-110.

[15]Maia CLB, Furtado ES, 2016. A study about psychophysiological measures in user experience monitoring and evaluation. Proc 15^th Brazilian Symp on Human Factors in Computing Systems, p.7.

[16]Mitterschiffthaler MT, Fu CHY, Dalton JA, et al., 2007. A functional MRI study of happy and sad affective states induced by classical music. Hum Brain Mapp, 28(11):1150-1162.

[17]Mori K, Iwanaga M, 2017. Two types of peak emotional responses to music: the psychophysiology of chills and tears. Sci Rep, 7:46063.

[18]Nyklíček I, Thayer JF, van Doornen LJP, 1997. Cardiorespiratory differentiation of musically-induced emotions. J Psychophysiol, 11(4):304-321.

[19]Pedregosa F, Varoquaux G, Gramfort A, et al., 2011. Scikit-learn: machine learning in Python. J Mach Learn Res, 12(10):2825-2830.

[20]Picard RW, Vyzas E, Healey J, 2001. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Patt Anal Mach Intell, 23(10):1175-1191.

[21]Rani P, Liu C, Sarkar N, et al., 2006. An empirical study of machine learning techniques for affect recognition in human-robot interaction. Patt Anal Appl, 9(1):58-69.

[22]Ren P, Barreto A, Gao Y, et al., 2013. Affective assessment by digital processing of the pupil diameter. IEEE Trans Affect Comput, 4(1):2-14.

[23]Tzanetakis G, Cook P, 2000. MARSYAS: a framework for audio analysis. Organ Sound, 4(3):169-175.

[24]Wagner J, Kim J, Andre E, 2005. From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. IEEE Int Conf on Multimedia and Expo, p.940-943.

[25]Wang SH, Phillips P, Dong ZC, et al., 2018. Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm. Neurocomputing, 272:668-676.

[26]Wen WS, Liu GY, Cheng NP, et al., 2014. Emotion recognition based on multi-variant correlation of physiological signals. IEEE Trans Affect Comput, 5(2):126-140.

[27]Xing BX, Zhang KJ, Sun SQ, et al., 2015. Emotion-driven Chinese folk music-image retrieval based on DE-SVM. Neurocomputing, 148:619-627.

[28]Yang YH, Chen HH, 2011. Prediction of the distribution of perceived music emotions using discrete samples. IEEE Trans Audio Speech Lang Process, 19(7):2184-2196.

[29]Yun T, Guan L, 2013. Human emotional state recognition using real 3D visual features from Gabor library. Patt Recogn, 46(2):529-538.

[30]Zhang YD, Yang ZJ, Lu HM, et al., 2016. Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access, 4:8375-8385.

[31]Zhu X, 2010. Emotion recognition of EMG based on BP neural network. Proc 2^nd Int Symp on Networking and Network Security, p.227-229.