期刊论文详细信息
Frontiers in Psychology
Modeling Music Emotion Judgments Using Machine Learning Methods
Naresh N. Vempala1 
关键词: music cognition;    music emotion;    physiological responses;    computational modeling;    neural networks;    machine learning;    random forests;   
DOI  :  10.3389/fpsyg.2017.02239
学科分类:心理学(综合)
来源: Frontiers
PDF
【 摘 要 】

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.

【 授权许可】

CC BY   

【 预 览 】
附件列表
Files Size Format View
RO201904027863660ZK.pdf 1628KB PDF download
  文献评价指标  
  下载次数:4次 浏览次数:6次