音乐元素与情感关系的跨文化分析

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xin Wang, Yujia Wei, Dasheng Yang
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引用次数: 2

摘要

在跨文化背景下,探索影响各种类型音乐的音乐元素的文化特殊性和普遍性,有利于个性化的情感识别。本研究引入高阶音乐元素,探讨其对情绪知觉的影响。通过比较不同文化音乐的音乐情感识别(MER)模型,进一步确定具有文化普遍性和文化特殊性的音乐元素。参与者对四种古典音乐(中国合奏、中国独奏、西方合奏和西方独奏)的效价、紧张唤醒和能量唤醒进行打分。通过人工评价或自动算法对音色、节奏、发音、动态和音域5类15个音乐元素进行标注。通过偏最小二乘回归分析了音乐情感与音乐要素之间的关系。结果表明,节奏、节奏复杂性和发音在文化上具有普遍性;与音色、音域和动态特征相关的音乐元素是文化特有的。通过提高节奏、节奏复杂性、断音、效价感知、紧张唤醒和能量唤醒可以有效地改善。基于偏最小二乘回归(PLSR)模型对数据集的分析结果表明,手工和自动相结合的音乐元素标注可以提高MER系统的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cross-cultural analysis of the correlation between musical elements and emotion

Cross-cultural analysis of the correlation between musical elements and emotion

In a cross-cultural context, exploring musical elements' cultural specificity and universality that affect various types of music is conducive to personalised emotion recognition. In this study, high-level musical elements are introduced to explore their influence on emotional perception. By comparing music emotion recognition (MER) models of varied cultural music, musical elements with cultural universality and cultural specificity are further determined. Participants rated valence, tension arousal, and energy arousal on labelled nine-point analogical–categorical scales for four types of classical music: Chinese ensemble, Chinese solo, Western ensemble, and Western solo. Fifteen musical elements in five categories—timbre, rhythm, articulation, dynamics, and register were annotated through manual evaluation or the automatic algorithm. The relationship between music emotion and musical elements was analysed through partial least squares regression. Results showed that tempo, rhythm complexity, and articulation are culturally universal; musical elements related to timbre, register, and dynamics features are culturally specific. By increasing tempo, rhythm complexity, staccato, perception of valence, tension arousal, and energy arousal can be effectively improved. Based on the Partial least squares regression (PLSR) model's results for the datasets, the combination of manual and automatic annotation for musical elements can improve the MER system's performance.

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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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