Vempala Naresh N, Russo Frank A
SMART Lab, Department of Psychology, Ryerson University, Toronto, ON, Canada.
Toronto Rehabilitation Institute, Toronto, ON, Canada.
Front Psychol. 2018 Jan 5;8:2239. doi: 10.3389/fpsyg.2017.02239. eCollection 2017.
Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.
从60名聆听60段音乐摘录的参与者那里获取了情绪判断和五个生理数据通道。使用了各种机器学习（ML）方法对情绪判断进行建模，包括神经网络、线性回归和随机森林。感知情绪模型的输入由从音乐录音中提取的音频特征组成。感受情绪模型的输入由从生理记录中提取的生理特征组成。在考虑认知主义者和情感主义者之间关于音乐情感的经典辩论的情况下对模型进行了训练和解释。我们的模型支持一种混合立场，即情绪判断受到感知情绪和感受情绪组合的影响。在比较用于建模的不同ML方法时，我们得出结论，神经网络是最优的，产生的模型既灵活又可解释。对一个包含多个网络的委员会机器的检查表明，唤醒判断主要受感受情绪的影响，而效价判断主要受感知情绪的影响。
