Department of Psychology, University of Geneva, Boulevard du Pont-d'Arve, 40, Geneva CH-1211, Switzerland.
Department of Speech Music Hearing, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm SE-10044, Sweden.
J Acoust Soc Am. 2017 Oct;142(4):1805. doi: 10.1121/1.5002886.
There has been little research on the acoustic correlates of emotional expression in the singing voice. In this study, two pertinent questions are addressed: How does a singer's emotional interpretation of a musical piece affect acoustic parameters in the sung vocalizations? Are these patterns specific enough to allow statistical discrimination of the intended expressive targets? Eight professional opera singers were asked to sing the musical scale upwards and downwards (using meaningless content) to express different emotions, as if on stage. The studio recordings were acoustically analyzed with a standard set of parameters. The results show robust vocal signatures for the emotions studied. Overall, there is a major contrast between sadness and tenderness on the one hand, and anger, joy, and pride on the other. This is based on low vs high levels on the components of loudness, vocal dynamics, high perturbation variation, and a tendency for high low-frequency energy. This pattern can be explained by the high power and arousal characteristics of the emotions with high levels on these components. A multiple discriminant analysis yields classification accuracy greatly exceeding chance level, confirming the reliability of the acoustic patterns.
关于歌唱声音中情感表达的声学相关性，研究甚少。在这项研究中，我们探讨了两个相关问题：歌手对音乐作品的情感诠释如何影响歌唱发声中的声学参数？这些模式是否足够具体，能够进行统计区分预期的表达目标？八位专业歌剧演唱者被要求（使用无意义的内容）用音阶上行和下行演唱，以表达不同的情绪，就像在舞台上一样。用一套标准参数对录音室的录音进行了声学分析。结果显示出所研究的情感具有明显的声乐特征。总的来说，悲伤和温柔与愤怒、喜悦和自豪之间存在着很大的对比。这是基于响度、声音动态、高扰动变化和低频能量倾向的组件的高低水平。这种模式可以用具有高水平组件的情绪的高能量和唤醒特性来解释。多元判别分析得出的分类准确率大大超过了随机水平，证实了声学模式的可靠性。
