Cross‐cultural analysis of the correlation between musical elements and emotion. Issue 2 (20th September 2021)

• Record Type: Journal Article
• Authors: Wang, Xin; Wei, Yujia; Yang, Dasheng
• Other Names: Li Zijin guestEditor.; McAdams Stephen guestEditor.
• Is Part Of: Cognitive computation and systems. Volume 4:Issue 2(2022)
• Page Start: 116
• Page End: 129
• Publication Date: 2021-09-20
• Subjects: cross‐culture -- emotion perception -- musical elements -- music emotion recognition; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12032
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: 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.