USE OF GAUSSIAN PROCESS TO MODEL, PREDICT AND EXPLAIN HUMAN EMOTIONAL RESPONSE TO CHINESE TRADITIONAL MUSIC

Jun Su and Peng Zhou ()
Additional contact information
Jun Su: College of Music, Chengdu Normal University, Chengdu 611130, P. R. China
Peng Zhou: ��Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, P. R. China

Advances in Complex Systems (ACS), 2021, vol. 24, issue 06, 1-22

Abstract: Music listening is one of the most enigmatic of human mental phenomena; it not only triggers emotions but also changes our behavior. During the music session many people are observed to exhibit varying emotional response, which can be influenced by diverse factors such as music genre and instrument as well as the personal attributes of audiences. In this study, we assume that there is an intrinsic, complex and implicit relationship between the basic sound features of music and human emotional response to the music. The response levels of 12 individuals to a representative repertoire of 36 classical/popular Chinese traditional music (CTM) are systematically analyzed using the chills as a quantitative indicator, totally resulting in 432 (12×36) CTM–individual pairs that define a systematic individual-to-music response profile (SPTMRP). Gaussian process (GP) is then employed to model the multivariate correlation of SPTMRP profile with 15 sound features (including 5 Timbres, 4 Rhythms and 6 Pitchs) and 5 individual features in a supervised manner, which is also improved by genetic algorithm (GA) feature selection and compared with other machine learning methods. It is shown that the built GP regression model possesses a strong internal fitting ability (rF2=0.786) and a good external predictive power (rP2=0.593), which performed much better than linear PLS and nonlinear SVM and RF, confirming that the human emotional response to music can be quantitatively explained by GP methodology. Statistical examination of the GP model reveals that the sound features contribute more significantly to emotional response than individual features; their importance increases in the order: PitchKeywords: Gaussian process; Chinese traditional music; emotional response; multivariate analysis; machine learning; sound feature (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525922500011
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:24:y:2021:i:06:n:s0219525922500011

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525922500011

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().