Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognitio...Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.展开更多
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 n...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 and0.014 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 and0.015 76 for valence.We provide an effective way to recognize personal music emotional experience,and the study can be applied to personalized music recommendation.展开更多
基金Supported by the National Natural Science Foundation of China (No.60873143)the National Key Subject Foundation for Basic Psychology (No.NKSF07003)
文摘Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.
基金Project supported by the Philosophy and Social Science Planning Fund Project of Zhejiang Province,China(No.20NDQN297YB)the National Natural Science Foundation of China(No.61702454)
文摘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 and0.014 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 and0.015 76 for valence.We provide an effective way to recognize personal music emotional experience,and the study can be applied to personalized music recommendation.
