音色
脑电图
计算机科学
语音识别
相关性(法律)
人工智能
情感(语言学)
情绪分类
音乐与情感
音乐剧
模式识别(心理学)
心理学
音乐教育
沟通
音乐
艺术
精神科
政治学
法学
视觉艺术
教育学
作者
Gang Luo,Shuting Sun,Kun Qian,Bin Hu,Björn W. Schuller,Yoshiharu Yamamoto
标识
DOI:10.1109/embc40787.2023.10339971
摘要
Music can effectively induce specific emotion and usually be used in clinical treatment or intervention. The electroencephalogram can help reflect the impact of music. Previous studies showed that the existing methods achieved relatively good performance in predicting emotion response to music. However, these methods tend to be time consuming and expensive due to their complexity. To this end, this study proposes a grey wolf optimiser-based method to predict the induced emotion through fusing electroencephalogram features and music features. Experimental results show that, the proposed method can reach a promising performance for predicting emotional response to music and outperform the alternative method. In addition, we analyse the relationship between the music features and electroencephalogram features and the results demonstrate that, musical timbre features are significantly related to the electroencephalogram features.Clinical relevance— This study targets the automatic prediction of the human response to music. It further explores the correlation between EEG features and music features aiming to provide the basis for the extension to the application of music. The grey wolf optimiser-based method proposed in this study could supply a promising avenue for the emotion prediction as induced by music.
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