Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.     

游客情感计算的文本大数据挖掘方法比较研究

旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利,已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论,以文本大数据为数据源,在全面梳理国内外情感计算相关成果的基础上,利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘,探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型,其核心是构建情感词典和设计情感计算规则,方法简单,容易实现,适用语料范围广。（2）机器学习,用统计学方法抽取文本中的特征项,具有非线性特征,可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算,效果良好,准确率在85%以上。训练多领域的文本语料易于移植,实用性强,且泛化能力好,较适合大数据时代游客情感计算研究。     

基于ZCPA参数的语音情感识别研究

主要讨论了情感语音特征参数的提取、语音情感的分类、语音资料的获取和应用连续隐马可夫模型进行情感识别等,重点比较了ZCPA特征参数和传统特征参数在不同噪声环境下的识别率,实验表明,在不同的噪声环境下,采用ZCPA特征的语音情感的识别效果较好,识别率也没有明显的下降。     

基于SVM的语音情感识别研究

随着计算机技术的发展,人们对和谐人机交互的要求不断提高,这就要求计算机能理解说话人的情感信息,即能进行语音情感识别。本文提出了一种基于支持向量机（SVM）的语音情感识别方法,主要对人类的6种基本情感：高兴、惊奇、愤怒、悲伤、恐惧、平静进行研究。首先对自建语音情感数据库的情感语句提取特征,然后运用序列前向选择（SFS）算...     

基于命令-个性的虚拟士兵情绪计算模型研究

针对虚拟士兵个性的复杂性及情绪状态受命令影响较大的问题,将具体的命令及个性的特征进行参数化描述,并定义命令属性和个性属性。通过命令和个性的参数化描述定义情绪刺激因子,分析命令、个性、情绪刺激因子的关系。最终建立命令-个性的虚拟士兵情绪计算模型。实验表明,该模型能较好地计算出虚拟士兵在命令和个性驱动下的情绪状态     

Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning

Speech emotion recognition (SER) in noisy environment is a vital issue in artificial intelligence (AI). In this paper, the reconstruction of speech samples removes the added noise. Acoustic features extracted from the reconstructed samples are selected to build an optimal feature subset with better emotional recognizability. A multiple-kernel (MK) support vector machine (SVM) classifier solved by semi-definite programming (SDP) is adopted in SER procedure. The proposed method in this paper is demonstrated on Berlin Database of Emotional Speech. Recognition accuracies of the original, noisy, and reconstructed samples classified by both single-kernel (SK) and MK classifiers are compared and analyzed. The experimental results show that the proposed method is effective and robust when noise exists.     

Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.     

实用语音情感的特征分析与识别的研究

 该文针对语音情感识别在实际中的应用,研究了烦躁等实用语音情感的分析与识别。通过计算机游戏诱发的方式采集了高自然度的语音情感数据,提取了74种情感特征,分析了韵律特征、音质特征与情感维度之间的关系,对烦躁等实用语音情感的声学特征进行了评价与选择,提出了针对实际应用环境的可拒判的实用语音情感识别方法。实验结果表明,文中采用的语音情感特征,能较好识别烦躁等实用语音情感,平均识别率达到75%以上。可拒判的实用语音情感识别方法,对模糊的和未知的情感类别的分类进行了合理的决策,在语音情感的实际应用中具有重要的意义。     

基于LS-SVM的情感语音识别

提出了一种基于LS-SVM的情感语音识别方法。即先提取实验中语音信号的基频,能量,语速等参数为情感特征,然后采用LS-SVM方法对相应的情感语音信号建立模型,进行识别。实验结果表明,利用LS-SVM进行基本情感识别时,识别率较高。     

结合音质特征和韵律特征的语音情感识别

为了提高语音情感的正确识别率,在情感语音韵律特征的基础上,提出情感语音音质特征的提取.结合音质特征参数和韵律特征参数,采用支持向量机分类器实现汉语普通话生气、高兴、悲伤和惊奇四种主要情感类型语音的情感识别.实验结果表明,语音音质特征参数和韵律特征参数相结合取得的情感平均正确识别率为88.1%,比单独使用韵律特征参数高出6%.可见,语音音质特征是一种较有效的情感特征参数.