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Popular Music as Entertainment Communication: How Perceived Semantic Expression Explains Liking of Previously Unknown Music

机译:受欢迎的音乐作为娱乐沟通:如何看待语义表达式解释了以前未知的音乐

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Our contribution addresses popular music as essential part of media entertainment offerings. Prior works explained liking for specific music titles in ‘push scenarios’ (radio programs, music recommendation, curated playlists) by either drawing on personal genre preferences, or on findings about ‘cognitive side effects’ leading to a preference drift towards familiar and society-wide popular tracks. However, both approaches do not satisfactorily explain why previously unknown music is liked. To address this, we hypothesise that unknown music is liked the more it is perceived as emotionally and semantically expressive, a notion based on concepts from media entertainment research and popular music studies. By a secondary analysis of existing data from an EU-funded R&D project, we demonstrate that this approach is more successful in predicting 10000 listeners’ liking ratings regarding 549 tracks from different genres than all hitherto theories combined. We further show that major expression dimensions are perceived relatively homogeneous across different sociodemographic groups and countries. Finally, we exhibit that music is such a stable, non-verbal sign-carrier that a machine learning model drawing on automatic audio signal analysis is successfully able to predict significant proportions of variance in musical meaning decoding.
机译:我们的贡献涉及流行音乐作为媒体娱乐产品的重要组成部分。先前的作品通过绘制个人类型偏好,或关于“认知副作用”导致偏好偏好朝向熟悉和社会的偏好偏好的调查结果,对“推送场景”(广播节目广阔的流行轨道。然而,两种方法都不令人满意地解释为什么先前未知的音乐被人喜欢。为了解决这个问题,我们假设未知的音乐被认为是情感和语义表达的越多,这是一个基于媒体娱乐研究和流行音乐研究的概念的概念。通过对欧盟资助研发项目的现有数据的二次分析,我们证明这种方法更成功地预测来自不同类型的549个曲目的10000听众的喜好评级,而不是所有迄今为止所结合的。我们进一步表明,各种表达尺寸在不同的社会形象组和国家的相对均匀地被察觉。最后,我们展示了音乐是一种如此稳定的非语言标志载体,即自动音频信号分析的机器学习模型绘制成功地预测音乐效果解码中的显着方差比例。

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