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

机译:跨音高寄存器的乐器声音情感品质的感知和建模

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摘要

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 (positiveegative 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.
机译:作曲家通常会选择特定的乐器在音乐中传达给定的情绪基调,部分是由于其表现力的可能性,也归因于他们在特定音色和给定动态标记中的音色。从计算的角度来看,音乐心理学和音乐信息学都感兴趣的是在给定音高下引起音色的声学特性与音调的感知情感质量之间的关系。向音乐家和非音乐家听众展示了137种音调,这些音调在每种乐器的整个音高范围内以固定的动态标记(强音)弹奏,音高为D#,并且从铜管乐器,木管乐器,弦乐器和音高中汲取了不同的标准管弦乐器演奏技巧打击乐家庭。他们根据情绪效价(正/负和愉悦/不愉快),能量唤醒(清醒/疲倦),紧张唤醒(兴奋/镇静),喜好(喜欢/不喜欢)和熟悉程度,在六个类比等级上对每种语气进行评分。线性混合模型揭示了音乐训练,乐器系列和音高寄存器的交互作用,音高寄存器与几个因变量之间具有非线性关系。为每种声音计算了Timbre工具箱中的23个音频描述符,并通过两种方式进行了分析:线性偏最小二乘回归(PLSR)和非线性人工神经网络建模。这两种分析在解释情绪等级时,在各种频谱,时间和频谱时态音频描述符的重要性方面趋于一致,但是也出现了一些差异。音频描述符的不同组合对三个情感维度做出了重要贡献,表明它们具有独特的声学特性。在较低的频谱斜率,更大的强偏音出现以及幅度包络中,价态更正,其具有更强的起音和更早的衰减。明亮的声音,更大的频谱变化和更柔和的打击带来更高的紧张感。更大的能量唤醒与更明亮的声音,更高的频谱质心和更慢的频谱斜率降低以及更大的频谱出现有关。讨论了线性方法和非线性方法之间的差异。

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