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.

中文翻译：

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流行音乐作为娱乐传播：感知的语义表达如何解释喜欢先前未知的音乐

我们的贡献致力于将流行音乐作为媒体娱乐产品的重要组成部分。先前的作品通过利用个人流派偏好或关于“认知副作用”的发现导致偏好向熟悉和社会偏向的“推动情景”（广播节目，音乐推荐，精选播放列表）中的特定音乐标题进行了解释，热门曲目。但是，两种方法都不能令人满意地解释为什么喜欢以前未知的音乐。为了解决这个问题，我们假设未知音乐越喜欢它在情感和语义上的表达，即基于媒体娱乐研究和流行音乐研究的概念。通过对欧盟资助的研发项目中现有数据的二次分析，我们证明，这种方法在预测549种不同流派曲目的10000名听众的喜好度方面比迄今为止所有理论都更为成功。我们进一步表明，在不同的社会人口学群体和国家之间，主要表达维度被认为是相对同质的。最后，我们证明音乐是一种稳定的非语言符号载体，以自动音频信号分析为基础的机器学习模型可以成功地预测音乐意义解码中很大比例的方差。