协和/不协和的解剖:评估声学和文化的预测跨多个数据集与和弦

Q1 Arts and Humanities
T. Eerola, Imre Lahdelma
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引用次数: 16

摘要

和声和不谐音感知的声学和音乐成分最近已被确定。本研究通过三种分析操作扩大了和谐和不和谐的预测范围。在实验1中,我们使用层次聚类分析从广泛的和弦数据集中提取了一些和声和不谐音的中心预测因子的潜在结构。确定了四个特征类别,在很大程度上确认了现有的三个类别(粗糙度,谐波,熟悉度),包括光谱包络线作为一个额外的类别,与这些分开。在实验2中,我们通过分析先前发表的三个数据集来评估Harrison和Pearce当前的和谐/不和谐模型。我们使用线性混合模型来优化预测因子的选择,并提供一个修正模型。我们还提出并评估了一些代表熟悉度的新预测因子。在实验3中,Harrison和Pearce的模型和我们的修正模型用9个数据集进行评估,这些数据集提供了一致性和不一致性的经验平均评级。结果表明Harrison和Pearce模型的预测率很好(62%),修正模型的预测率仍然明显更好(73%)。在修正后的模型中,Harrison和Pearce模型的和声预测因子被Stolzenburg模型所取代,通过简化和弦分类编码的熟悉度预测因子取代了原来基于语料库的模型。谱包络线作为一个新类别的加入是对和声/不谐音评级的一个小小的补充。关于和谐/不和谐的解剖,我们分析了预测因子的共线性,这是通过实验3中所有预测因子的主成分分析来解决的。这将谐波和粗糙度预测因子捕获到一个组件中;总体而言,这三个组成部分占66%的和谐/不和谐评分,其中主要方差来自熟悉度(46.2%),其次是粗糙度/和谐度(19.3%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Anatomy of Consonance/Dissonance: Evaluating Acoustic and Cultural Predictors Across Multiple Datasets with Chords
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%).
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来源期刊
Music  Science
Music Science Arts and Humanities-Music
CiteScore
2.80
自引率
0.00%
发文量
15
审稿时长
10 weeks
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