Using psychophysiological measures to recognize personal music emotional experience

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.