Tagging online music contents for emotion. A systematic approach based on contemporary emotion research

目前在线数据库中的音乐标签主要依赖于音乐流派和艺术家姓名，音乐标签通常是模糊和不准确的。然而，音乐体验最显著的特征可能是情感。到目前为止，很少有人尝试给音乐贴上情绪或情感的标签，这在情感研究中缺乏科学基础。目前的项目建议将最近对音乐诱发情感的研究纳入越来越多的在线音乐数据库和目录中，特别是日内瓦情感音乐量表（GEMS）-我们小组最近开发的一种描述音乐情感效果的评级措施。具体来说，这里的目标是开发GEMS成为一个创新的概念和技术工具，用于标记在线音乐内容的情感。为此，提出了三项研究。在研究1中，我们将研究GEMS标签及其分组是否比最初用于其发展的音乐类型更广泛。在研究2中，我们将使用先进的数据简化技术来选择最经常和最重要的标签来描述音乐诱发的情绪。在第三项研究中，我们将研究新的GEMS相比传统的方法来标记音乐的额外好处。调查结果的预期影响有三个方面。首先，接下来要描述的研究将促进我们对音乐引起的情感的性质和结构的理解。建立一个有效的音乐诱发情绪模型对于社会科学和神经科学领域的研究具有重要意义。第二，音乐信息的组织和检索可以受益于一个科学合理和简约的分类法来描述音乐的情感效果。因此，对相关在线音乐数据库的搜索不需要再局限于流派或艺术家，而是还可以将情感作为音乐的关键体验维度。第三，一个有效的情感标记方案可以帮助研究人员和专业人士选择音乐来诱导特定的情感。例如，心理学家、行为经济学家和神经科学家经常需要在他们的实验中诱导情绪，以了解情绪如何调节行为或表现。在受控环境中，音乐是情感诱导的一个明显选择，因为它是一种通用语言，适合于跨文化的比较，因为它在伦理上是没有问题的。

Current approaches to the tagging of music in online databases predominantly rely on music genre and artist name, with music tags being often ambiguous and inexact. Yet, the possibly most salient feature musical experiences is emotion. The few attempts so far undertaken to tag music for mood or emotion lack a scientific foundation in emotion research. The current project proposes to incorporate recent research on music-evoked emotion into the growing number of online musical databases and catalogues, notably the Geneva Emotional Music Scale (GEMS) - a rating measure for describing emotional effects of music recently developed by our group. Specifically, the aim here is to develop the GEMS into an innovative conceptual and technical tool for tagging of online musical content for emotion. To this end, three studies are proposed. In study 1, we will examine whether the GEMS labels and their grouping holds up against a much wider range of musical genres than those that were originally used for its development. In Study 2, we will use advanced data reduction techniques to select the most recurrent and important labels for describing music-evoked emotion. In a third study we will examine the added benefit of the new GEMS compared to conventional approaches to the tagging of music. The anticipated impact of the findings is threefold. First, the research to be described next will advance our understanding of the nature and structure of emotions evoked by music. Developing a valid model of music-evoked emotion is crucial for meaningful research in the social and in the neurosciences. Second, music information organization and retrieval can benefit from a scientifically sound and parsimonious taxonomy for describing the emotional effects of music. Thus, searches for relevant online music databases need not be longer confined to genre or artist, but can also incorporate emotion as a key experiential dimension of music. Third, a valid tagging scheme for emotion can assist both researchers and professionals in the choice of music to induce specific emotions. For example, psychologists, behavioural economists, and neuroscientists often need to induce emotion in their experiments to understand how behaviour or performance is modulated by emotion. Music is an obvious choice for emotion induction in controlled settings because it is a universal language that lends itself to comparisons across cultures and because it is ethically unproblematic.