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合成音乐音色的感知缩放:共同维度、特异性和潜在主体类别。

Perceptual scaling of synthesized musical timbres: common dimensions, specificities, and latent subject classes.

作者信息

McAdams S, Winsberg S, Donnadieu S, De Soete G, Krimphoff J

机构信息

Laboratoire de Psychologie Expérimentale (CNRS), Université René Descartes, EPHE, Paris, France.

出版信息

Psychol Res. 1995;58(3):177-92. doi: 10.1007/BF00419633.

Abstract

To study the perceptual structure of musical timbre and the effects of musical training, timbral dissimilarities of synthesized instrument sounds were rated by professional musicians, amateur musicians, and nonmusicians. The data were analyzed with an extended version of the multidimensional scaling algorithm CLASCAL (Winsberg & De Soete, 1993), which estimates the number of latent classes of subjects, the coordinates of each timbre on common Euclidean dimensions, a specificity value of unique attributes for each timbre, and a separate weight for each latent class on each of the common dimensions and the set of specificities. Five latent classes were found for a three-dimensional spatial model with specificities. Common dimensions were quantified psychophysically in terms of log-rise time, spectral centroid, and degree of spectral variation. The results further suggest that musical timbres possess specific attributes not accounted for by these shared perceptual dimensions. Weight patterns indicate that perceptual salience of dimensions and specificities varied across classes. A comparison of class structure with biographical factors associated with degree of musical training and activity was not clearly related to the class structure, though musicians gave more precise and coherent judgments than did non-musicians or amateurs. The model with latent classes and specificities gave a better fit to the data and made the acoustic correlates of the common dimensions more interpretable.

摘要

为了研究音乐音色的感知结构以及音乐训练的影响,专业音乐家、业余音乐家和非音乐家对合成乐器声音的音色差异进行了评分。使用多维缩放算法CLASCAL(Winsberg & De Soete,1993)的扩展版本对数据进行分析,该算法估计受试者潜在类别的数量、每个音色在常见欧几里得维度上的坐标、每个音色独特属性的特异性值,以及每个潜在类别在每个常见维度和特异性集上的单独权重。对于具有特异性的三维空间模型,发现了五个潜在类别。常见维度通过对数上升时间、频谱质心和频谱变化程度进行心理物理学量化。结果进一步表明,音乐音色具有这些共享感知维度无法解释的特定属性。权重模式表明,维度和特异性的感知显著性在不同类别之间有所不同。尽管音乐家比非音乐家或业余爱好者给出了更精确和连贯的判断,但将类别结构与与音乐训练程度和活动相关的个人因素进行比较,与类别结构并没有明显的关联。具有潜在类别和特异性的模型对数据的拟合更好,并且使常见维度的声学相关性更易于解释。

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