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跨音高范围对乐器声音情感特质的感知与建模

Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers.

作者信息

McAdams Stephen, Douglas Chelsea, Vempala Naresh N

机构信息

Music Research, Schulich School of Music, McGill University Montreal, QC, Canada.

Department of Psychology, Ryerson University Toronto, ON, Canada.

出版信息

Front Psychol. 2017 Feb 8;8:153. doi: 10.3389/fpsyg.2017.00153. eCollection 2017.

Abstract

Composers often pick specific instruments to convey a given emotional tone in their music, partly due to their expressive possibilities, but also due to their timbres in specific registers and at given dynamic markings. Of interest to both music psychology and music informatics from a computational point of view is the relation between the acoustic properties that give rise to the timbre at a given pitch and the perceived emotional quality of the tone. Musician and nonmusician listeners were presented with 137 tones produced at a fixed dynamic marking (forte) playing tones at pitch class D# across each instrument's entire pitch range and with different playing techniques for standard orchestral instruments drawn from the brass, woodwind, string, and pitched percussion families. They rated each tone on six analogical-categorical scales in terms of emotional valence (positive/negative and pleasant/unpleasant), energy arousal (awake/tired), tension arousal (excited/calm), preference (like/dislike), and familiarity. Linear mixed models revealed interactive effects of musical training, instrument family, and pitch register, with non-linear relations between pitch register and several dependent variables. Twenty-three audio descriptors from the Timbre Toolbox were computed for each sound and analyzed in two ways: linear partial least squares regression (PLSR) and nonlinear artificial neural net modeling. These two analyses converged in terms of the importance of various spectral, temporal, and spectrotemporal audio descriptors in explaining the emotion ratings, but some differences also emerged. Different combinations of audio descriptors make major contributions to the three emotion dimensions, suggesting that they are carried by distinct acoustic properties. Valence is more positive with lower spectral slopes, a greater emergence of strong partials, and an amplitude envelope with a sharper attack and earlier decay. Higher tension arousal is carried by brighter sounds, more spectral variation and more gentle attacks. Greater energy arousal is associated with brighter sounds, with higher spectral centroids and slower decrease of the spectral slope, as well as with greater spectral emergence. The divergences between linear and nonlinear approaches are discussed.

摘要

作曲家常常挑选特定乐器在其音乐中传达特定的情感基调,部分原因在于这些乐器具有的表现力,但也因其在特定音区以及给定力度标记下的音色。从计算角度来看,音乐心理学和音乐信息学都感兴趣的是,在给定音高上产生音色的声学特性与音调的感知情感特质之间的关系。向音乐家和非音乐家听众呈现了137个以固定力度标记(强音)演奏的音调,这些音调在每个乐器的整个音域内演奏D#音级,并且使用了来自铜管乐器、木管乐器、弦乐器和有音高打击乐器家族的标准管弦乐器的不同演奏技巧。他们根据情感效价(积极/消极和愉悦/不悦)、能量唤起(清醒/疲惫)、紧张唤起(兴奋/平静)、偏好(喜欢/不喜欢)和熟悉度,在六个类比分类量表上对每个音调进行评分。线性混合模型揭示了音乐训练、乐器家族和音区之间的交互作用,以及音区与几个因变量之间的非线性关系。为每个声音计算了来自音色工具箱的23个音频描述符,并以两种方式进行分析:线性偏最小二乘回归(PLSR)和非线性人工神经网络建模。这两种分析在各种频谱、时间和频谱-时间音频描述符对解释情感评分的重要性方面趋于一致,但也出现了一些差异。音频描述符的不同组合对三个情感维度有重大贡献,表明它们由不同的声学特性承载。效价在较低的频谱斜率、较强泛音的更多出现以及具有更尖锐起音和更早衰减的幅度包络下更积极。更高的紧张唤起由更明亮的声音、更多的频谱变化和更柔和的起音承载。更大的能量唤起与更明亮的声音、更高的频谱质心和频谱斜率的更缓慢下降以及更大的频谱出现相关。讨论了线性和非线性方法之间的差异。

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