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首页> 外文期刊>Music & Science >The Anatomy of Consonance/Dissonance: Evaluating Acoustic and Cultural Predictors Across Multiple Datasets with Chords
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The Anatomy of Consonance/Dissonance: Evaluating Acoustic and Cultural Predictors Across Multiple Datasets with Chords

机译：和谐/解剖的解剖学：评估具有和弦的多个数据集的声学和文化预测因子

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Acoustic and musical components of consonance and dissonance perception have been recently identified. This study expands the range of predictors of consonance and dissonance by three analytical operations. In Experiment 1, we identify the underlying structure of a number of central predictors of consonance and dissonance extracted from an extensive dataset of chords using a hierarchical cluster analysis. Four feature categories are identified largely confirming the existing three categories (roughness, harmonicity, familiarity), including spectral envelope as an additional category separate from these. In Experiment 2, we evaluate the current model of consonance/dissonance by Harrison and Pearce by an analysis of three previously published datasets. We use linear mixed models to optimize the choice of predictors and offer a revised model. We also propose and assess a number of new predictors representing familiarity. In Experiment 3, the model by Harrison and Pearce and our revised model are evaluated with nine datasets that provide empirical mean ratings of consonance and dissonance. The results show good prediction rates for the Harrison and Pearce model (62%) and a still significantly better rate for the revised model (73%). In the revised model, the harmonicity predictor of Harrison and Pearce’s model is replaced by Stolzenburg’s model, and a familiarity predictor coded through a simplified classification of chords replaces the original corpus-based model. The inclusion of spectral envelope as a new category is a minor addition to account for the consonance/dissonance ratings. With respect to the anatomy of consonance/dissonance, we analyze the collinearity of the predictors, which is addressed by principal component analysis of all predictors in Experiment 3. This captures the harmonicity and roughness predictors into one component; overall, the three components account for 66% of the consonance/dissonance ratings, where the dominant variance explained comes from familiarity (46.2%), followed by roughness/harmonicity (19.3%).
机译：最近已经确定了和谐和不和谐感知的声学和音乐组件。本研究扩大了三种分析操作的共同和解剖的预测因子范围。在实验1中，我们使用分层聚类分析确定从和弦和不和谐提取的一致性和解剖的底层结构。在很大程度上识别出四个特征类别，确认现有的三类（粗糙度，谐波，熟悉程度），包括频谱信封作为与这些分开的附加类别。在实验2中，我们通过分析三个先前公布的数据集来评估Harrison和Pearce的Consonance / Assonscon的当前模型。我们使用线性混合模型来优化预测器的选择并提供修订的模型。我们还提出并评估了一些代表熟悉的新预测因素。在实验3中，通过哈里森和珍珠的模型以及我们的修订模型进行了评估，并用九个数据集进行了九个数据集，这些数据集提供了和谐和解剖的经验平均评级。结果表明，哈里森和珍珠型（62％）的良好预测率（62％），修订模型仍然明显更好（73％）。在修订模型中，哈里森和Pearce模型的谐波预测因子被Stolzenburg的模型所取代，并且通过简化的和弦分类编码的熟悉性预测因子替换了基于原始语料库的模型。将光谱信封作为新类别列为新类别是占核对和不和谐评级的次要补充。关于和谐/解剖的解剖学，我们分析了预测因子的共同性，这是通过实验3中所有预测因子的主要成分分析来解决的。这将谐波和粗糙度预测因子捕获到一个组件中;总体而言，三个组成部分占合并/不和谐评级的66％，其中解释的主要方差来自熟悉（46.2％），其次是粗糙度/谐波（19.3％）。

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