Classification of the emotional impact initiated by film sequencesAssessment of the emotional experience of a multimedia presentation consisting of video and audio.

电影序列引发的情感影响的分类评估由视频和音频组成的多媒体演示的情感体验。

基本信息

项目摘要

The concept Quality of Experience (QoE) tries to assess the subjective quality which an observer experiences when consuming multimedia content. In the current proposal, QoE should be extended by a new central component, namely the emotional state an observer experiences when watching a video. The core of our project is to extract emotionally relevant key features if videos using several machine learning approaches, which in turn are used to predict the experienced emotion of the observer.In order to realize this interdisciplinary research question technically, we draw upon and extend psychological models of emotion, which are then computationally implemented by engineers. Supervised machine learning approaches are trained by comparing the predicted with the actual emotional experience of an observer. This learning stage will use a crowdsourcing approach which allows to gather a large amount of data for relatively low costs. The large sample allows a broad generalizability of the results, and allows to investigate how personality characteristics of the observers modulate the emotional impact of the videos. The results from the online study will be cross-validated and extended in a controlled laboratory experiment. In this study, objective and indirect indicators of the experienced emotions are assessed (electromyography of facial expressions; codings of (micro-)emotions from video recordings of the observers). The interdisciplinary project is composed of researchers from psychology and computer engineers and works on the following research questions:a) Is it possible to determine a mapping of technical features onto emotionally relevant stimuli?b) Are psychological models able to correctly predict the experienced emotions based on relevant stimuli of the video and personality features of a person?Goals of the project are, on the one hand, to implement existing psychological theories of emotion, validate them using large data sets, and to develop them further based on these results. On the other hand, the technical framework should be employed to classify a large set of videos concerning the emotional impact on observers. Furthermore, the project will generate a standardized data base of videos, for which the emotional impact is very well known. We expect that such a data base will have a huge impact and benefit for future studies in the area of human emotions.
体验质量(QoE)的概念试图评估观察者在消费多媒体内容时体验到的主观质量。在当前的提案中,QoE应该通过一个新的中心组件来扩展,即观察者在观看视频时所经历的情绪状态。我们项目的核心是使用几种机器学习方法提取视频中与情感相关的关键特征,这些特征反过来又被用来预测观察者的情感体验。为了在技术上实现这个跨学科的研究问题,我们借鉴并扩展了情感的心理模型,然后由工程师计算实现。监督机器学习方法通过将预测与观察者的实际情感体验进行比较来训练。这一学习阶段将采用众包办法,以相对较低的成本收集大量数据。大样本允许广泛的概括性的结果,并允许调查观察者的个性特征如何调节视频的情感影响。在线研究的结果将在受控实验室实验中进行交叉验证和扩展。在这项研究中,客观和间接指标的经验丰富的情绪进行评估(肌电图的面部表情;编码的(微)情绪从视频记录的观察者)。该跨学科项目由心理学和计算机工程师的研究人员组成,并致力于以下研究问题:a)是否有可能确定技术特征与情感相关刺激的映射?B)心理模型是否能够根据视频的相关刺激和人的个性特征正确地预测所经历的情绪?该项目的目标是,一方面,实施现有的情绪心理学理论,使用大型数据集验证它们,并根据这些结果进一步发展它们。另一方面,技术框架应该被用来对大量的视频进行分类,这些视频涉及对观察者的情感影响。此外,该项目将生成一个标准化的视频数据库,其情感影响是众所周知的。我们希望这样的数据库将对人类情感领域的未来研究产生巨大的影响和益处。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Open Library for Affective Videos (OpenLAV)
情感视频开放库 (OpenLAV)
  • DOI:
    10.23668/psycharchives.5042
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Israel;Paukner;Schiestel;Diepold;Schönbrodt
  • 通讯作者:
    Schönbrodt
Measuring Implicit Motives with the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms
用图画故事练习(PSE)衡量隐性动机:专家编码的德国故事、图片和更新的图片规范数据库
  • DOI:
    10.1080/00223891.2020.1726936
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Schönbrodt;Hagemeyer;Brandstätter;Czikmantori;Gröpel;Hennecke;Israel;L. S. F;Schultheiss
  • 通讯作者:
    Schultheiss
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Professor Dr.-Ing. Klaus Diepold其他文献

Professor Dr.-Ing. Klaus Diepold的其他文献

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