Baker David J, Müllensiefen Daniel
Music Cognition and Computation Lab, School of Music and Dramatic Arts, Louisiana State UniversityBaton Rouge, LA, USA.
Music, Mind and Brain Lab, Department of Psychology, Goldsmiths, University of LondonLondon, UK.
Front Psychol. 2017 May 4;8:662. doi: 10.3389/fpsyg.2017.00662. eCollection 2017.
The music of Richard Wagner tends to generate very diverse judgments indicative of the complex relationship between listeners and the sophisticated musical structures in Wagner's music. This paper presents findings from two listening experiments using the music from Wagner's that explores musical as well as individual listener parameters to better understand how listeners are able to hear leitmotives, a compositional device closely associated with Wagner's music. Results confirm findings from a previous experiment showing that specific expertise with Wagner's music can account for a greater portion of the variance in an individual's ability to recognize and remember musical material compared to measures of generic musical training. Results also explore how acoustical distance of the leitmotives affects memory recognition using a chroma similarity measure. In addition, we show how characteristics of the compositional structure of the leitmotives contributes to their salience and memorability. A final model is then presented that accounts for the aforementioned individual differences factors, as well as parameters of musical surface and structure. Our results suggest that that future work in music perception may consider both individual differences variables beyond musical training, as well as symbolic features and audio commonly used in music information retrieval in order to build robust models of musical perception and cognition.
理查德·瓦格纳的音乐往往会引发非常多样的评判,这表明听众与瓦格纳音乐中复杂的音乐结构之间存在着复杂的关系。本文展示了两项听力实验的结果,这些实验使用了瓦格纳的音乐,探讨了音乐以及个体听众参数,以便更好地理解听众是如何能够听出主导动机的,主导动机是一种与瓦格纳音乐紧密相关的作曲手法。结果证实了之前一项实验的发现,即与一般音乐训练的指标相比,对瓦格纳音乐的特定专业知识能够在更大程度上解释个体识别和记忆音乐素材能力的差异。结果还使用色度相似性度量探索了主导动机的声学距离如何影响记忆识别。此外,我们展示了主导动机的作曲结构特征如何促成其显著性和可记忆性。然后提出了一个最终模型,该模型考虑了上述个体差异因素以及音乐表面和结构的参数。我们的结果表明,未来音乐感知方面的工作可能需要考虑音乐训练之外的个体差异变量,以及音乐信息检索中常用的符号特征和音频,以便构建强大的音乐感知和认知模型。