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期刊论文 7

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2022 1

2019 1

2015 2

2010 1

2009 1

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脑电图；情感识别；注意力机制；人格特征 1

音乐；情感识别；生理信号；小波变换 1

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Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

《信息与电子工程前沿（英文）》 2015年第16卷第5期页码 358-366 doi: 10.1631/FITEE.1400323

摘要： Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including -means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

关键词： Speech emotion recognition Unsupervised feature learning Neural network Affect computing

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UsingKinect for real-time emotion recognition via facial expressions

Qi-rong MAO,Xin-yu PAN,Yong-zhao ZHAN,Xiang-jun SHEN

《信息与电子工程前沿（英文）》 2015年第16卷第4期页码 272-282 doi: 10.1631/FITEE.1400209

摘要： Emotion recognition via facial expressions (ERFE) has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by Kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units (AUs) and feature point positions (FPPs) tracked by Kinect. A fusion algorithm based on improved emotional profiles (IEPs) and maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.

关键词： Kinect Emotion recognition Facial expression Real-time classification Fusion algorithm Support vector machine (SVM)

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Review of sentiment analysis: An emotional product development view

《工程管理前沿（英文）》页码 592-609 doi: 10.1007/s42524-022-0227-z

摘要： Conveying consumers’ specific emotions in new products, referred to as emotional product development or emotional design, is strategically crucial for manufacturers. Given that sentiment analysis (SA) can extract and analyze people’s opinions, sentiments, attitudes, and perceptions regarding different products/services, SA-based emotional design may provide manufacturers with real-time, direct, and rapid decision support. Despite its considerable advancements and numerous survey and review articles, SA is seldom considered in emotional design. This study is among the first efforts to conduct a thorough review of SA from the view of emotional design. The comprehensive review of aspect-level SA reveals the following: 1) All studies focus on extracting product features by mixing technical product features and consumers’ emotional perceptions. Consequently, such studies cannot capture the relationships between technical and emotional attributes and thus cannot convey specific emotions to the new products. 2) Most studies use the English language in SA, but other languages have recently received more interest in SA. Furthermore, after conceptualizing emotion as Kansei and introducing emotional product development and Kansei Engineering, a review of the data-driven emotional design is then conducted. A few efforts start to study emotional design with the help of SA. However, these studies only focus on either analyzing consumers’ preferences on product features or extracting emotional opinions from online reviews, thus cannot realize data-driven emotional product development. Finally, some research opportunities are provided. This study opens a broad door to aspect-level SA and its integration with emotional product development.

关键词： sentiment analysis emotion product development Kansei Engineering

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Overall HIEs deconstruction method and its application

Li LIN, Chengqi XUE,

《机械工程前沿（英文）》 2009年第4卷第4期页码 466-471 doi: 10.1007/s11465-009-0076-z

摘要： Based on the human interface elements (HIEs) as the emotional carriers, this paper researches the general analysis steps of hierarchical HIEs and puts forward a new systematical deconstruction method for Kansei engineering fields. A mobile phone is taken as the research subject, the emotional design rules are inferred based on the multiple-orientation information of human interfaces, and the emotional change law is grasped from a new perspective. The research on the deconstruction method promotes the study of Kansei engineering and is a significant guide and reference for the theory and practice of related product design fields.

关键词： product design relationship model human interface design rules emotion

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Research on a quantificational method for chromatic-light emotions of architectural wall coatings in landscape illumination

Ting WANG, Ma JIAN, Gang LIU, Xiaoxi LIU, Mingyu ZHANG

《结构与土木工程前沿（英文）》 2010年第4卷第4期页码 498-502 doi: 10.1007/s11709-010-0096-0

摘要： Research on quantificational methods for chromatic-light emotion is an effective way of finding out the laws of light cognitive impact and the solution to the problem on application of light color in landscape lighting. This paper took chromatic-light-induced-emotion towards architectural wall coatings in landscape as the research object. The methods in use were real-state simulation and laboratory quantitative study. Consequently, the study successfully extracted light emotional factors and the rule of predictability to assist landscape light professionals.

关键词： landscape illumination architectural wall coatings chromatic-light emotion quantitative research

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基于心理生理信号的个人音乐情感体验识别 Research Articles

Le-kai ZHANG, Shou-qian SUN, Bai-xi XING, Rui-ming LUO, Ke-jun ZHANG

《信息与电子工程前沿（英文）》 2019年第20卷第7期页码 964-974 doi: 10.1631/FITEE.1800101

摘要：重要节点识别对电网安全意义重大。但电网音乐能激发人的情感，这是一个心理生理过程。因此心理生理特征可用于识别个人音乐情感体验。提出一种新的基于人体生理特征的个人音乐情感识别方法。首先，建立一个基于音乐情感特征的数据库和一个基于听音乐产生的生理信号的数据库，包括心电、脉搏、皮温、呼吸和瞳孔直径变化等生理信号。然后，分别采用线性回归、岭回归、三种不同核的支持向量机、决策树、K近邻算法、多层感知器和Nu支持向量回归（NuSVR）方法，通过音乐特征和人体生理特征识别音乐情感。结果显示，NuSVR性能优于其他方法，其唤醒相关系数为0.7347（均方差为0.02323），效价相关系数为0.7902（均方差为0.01485）。最后，对不同数据集进行比较，结果表明所有特征（音乐特征和所有生理特征）数据集在识别中表现最好，唤醒相关系数为0.6499（均方差为0.02932），效价相关系数为0.7735（均方差为0.01576）。本文提供了一种有效的个人音乐情感体验识别方法，可用于个性化音乐推荐。

关键词：音乐；情感识别；生理信号；小波变换

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一种面向机器情感智能的人格引导型情感脑机接口 Research Article

李少杰,李伟,邢泽健,袁文杰,韦香玉,张晓炜,胡斌

《信息与电子工程前沿（英文）》 2022年第23卷第8期页码 1158-1173 doi: 10.1631/FITEE.2100489

摘要：情感脑机接口（brain–computer interfaces, BCIs）已成为在人机协作中实现情感智能的一个重要途径。然而，由于脑电图（electroencephalogram, EEG）信号的复杂性和情绪反应的个体差异性，设计一个可靠和有效的模型仍然是一个巨大挑战。考虑到不同人格特征的个体在情绪感知和反应过程中的差异，整合人格信息和脑电信号对情绪识别是有帮助的。鉴于此，提出一种人格引导的注意力神经网络，其可以利用人格信息学习更为有效的EEG表征以用于情感识别。具体来说，我们首先利用卷积神经网络提取脑电信号的时域和空域表征，进而设计一种特殊的卷积核同时学习大脑头皮不同区域间和区域内的EEG导联相关关系。其次，考虑到不同大脑头皮区域在情绪识别中可能发挥不同的作用，提出一种人格引导的区域注意力机制，以进一步探索区域内和区域间EEG导联的贡献。最后，设计一种基于注意力的长短期记忆网络（long short-term memory, LSTM）建模EEG信号的时域动态特征。在AMIGOS数据集（一个用于个人和群体的情感、人格特征和情绪多模态研究的数据集）的实验结果表明，本研究所提方法可以显著提升被试独立策略下情感识别的性能，并优于现有情感识别方法。

关键词：脑电图；情感识别；注意力机制；人格特征

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标题作者时间类型操作

Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

期刊论文

UsingKinect for real-time emotion recognition via facial expressions

Qi-rong MAO,Xin-yu PAN,Yong-zhao ZHAN,Xiang-jun SHEN

期刊论文

Review of sentiment analysis: An emotional product development view

期刊论文

Overall HIEs deconstruction method and its application