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使用建模方法探究感知内在重音的潜在原理。

Probing the Underlying Principles of Perceived Immanent Accents Using a Modeling Approach.

作者信息

Friberg Anders, Bisesi Erica, Addessi Anna Rita, Baroni Mario

机构信息

Department of Speech, Music and Hearing, Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Laboratory "Perception and Memory", Department of Neuroscience, Institut Pasteur, Paris, France.

出版信息

Front Psychol. 2019 Jun 7;10:1024. doi: 10.3389/fpsyg.2019.01024. eCollection 2019.

Abstract

This article deals with the question of how the perception of the "immanent accents" can be predicted and modeled. By immanent accent we mean any musical event in the score that is related to important points in the musical structure (e.g., tactus positions, melodic peaks) and is therefore able to capture the attention of a listener. Our aim was to investigate the underlying principles of these accented notes by combining quantitative modeling, music analysis and experimental methods. A listening experiment was conducted where 30 participants indicated perceived accented notes for 60 melodies, vocal and instrumental, selected from Baroque, Romantic and Post-tonal styles. This produced a large and unique collection of perceptual data about the perceived immanent accents, organized by styles consisting of vocal and instrumental melodies within Western art music. The music analysis of the indicated accents provided a preliminary list of musical features that could be identified as possible reasons for the raters' perception of the immanent accents. These features related to the score in different ways, e.g., repeated fragments, single notes, or overall structure. A modeling approach was used to quantify the influence of feature groups related to pitch contour, tempo, timing, simple phrasing, and meter. A set of 43 computational features was defined from the music analysis and previous studies and extracted from the score representation. The mean ratings of the participants were predicted using multiple linear regression and support vector regression. The latter method (using cross-validation) obtained the best result of about 66% explained variance ( = 0.81) across all melodies and for a selected group of raters. The independent contribution of each feature group was relatively high for pitch contour and timing (9.6 and 7.0%). There were also significant contributions from tempo (4.5%), simple phrasing (4.4%), and meter (3.9%). Interestingly, the independent contribution varied greatly across participants, implying different listener strategies, and also some variability across different styles. The large differences among listeners emphasize the importance of considering the individual listener's perception in future research in music perception.

摘要

本文探讨了如何预测和模拟对“内在重音”的感知这一问题。我们所说的内在重音是指乐谱中与音乐结构的重要节点相关的任何音乐事件(例如,节拍位置、旋律高峰),因此能够吸引听众的注意力。我们的目标是通过结合定量建模、音乐分析和实验方法来研究这些重音音符的潜在原理。我们进行了一项听力实验,30名参与者针对从巴洛克、浪漫主义和后调性风格中选取的60首声乐和器乐旋律指出他们感知到的重音音符。这产生了一个关于感知到的内在重音的庞大且独特的感知数据集,该数据集按西方艺术音乐中声乐和器乐旋律的风格进行组织。对所指出重音的音乐分析提供了一份音乐特征的初步清单,这些特征可被确定为评分者感知内在重音的可能原因。这些特征以不同方式与乐谱相关,例如重复片段、单个音符或整体结构。我们采用一种建模方法来量化与音高轮廓、节奏、节拍、简单乐句划分和节拍相关的特征组的影响。从音乐分析和先前研究中定义了一组43个计算特征,并从乐谱表示中提取出来。使用多元线性回归和支持向量回归预测参与者的平均评分。后一种方法(使用交叉验证)在所有旋律和一组选定评分者中获得了约66%的最佳解释方差结果(=0.81)。每个特征组对音高轮廓和节拍的独立贡献相对较高(分别为9.6%和7.0%)。节奏(4.5%)、简单乐句划分(4.4%)和节拍(3.9%)也有显著贡献。有趣的是,独立贡献在参与者之间差异很大,这意味着听众策略不同,并且在不同风格之间也存在一些变异性。听众之间的巨大差异强调了在未来音乐感知研究中考虑个体听众感知的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a12/6566653/569e501b8510/fpsyg-10-01024-g001.jpg

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