TY - GEN
T1 - Melodious Micro-frissons
T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019
AU - Rahman, Jessica Sharmin
AU - Gedeon, Tom
AU - Caldwell, Sabrina
AU - Jones, Richard
AU - Hossain, Md Zakir
AU - Zhu, Xuanying
N1 - Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - The relationship between music and human physiological signals has been a topic of interest among researchers for many years. Understanding this relationship can not only lead to more enhanced music therapy methods, but it may also help in finding a cure to mental disorders and epileptic seizures that are triggered by certain music. In this paper, we investigate the effects of 3 different genres of music in participants' Electrodermal Activity (EDA). Signals were recorded from 24 participants while they listened to 12 music stimuli. Various feature selection methods were applied to a number of features which were extracted from the signals. A simple neural network using Genetic Algorithm (GA) feature selection can reach as high as 96.8% accuracy in classifying 3 different music genres. Classification based on participants' subjective rating of emotion reaches 98.3% accuracy with the Statistical Dependency (SD) / Minimal Redundancy Maximum Relevance (MRMR) feature selection technique. This shows that human emotion has a strong correlation with different types of music. In the future this system can be used to distinguish music based on their positive of negative effect on human mental health.
AB - The relationship between music and human physiological signals has been a topic of interest among researchers for many years. Understanding this relationship can not only lead to more enhanced music therapy methods, but it may also help in finding a cure to mental disorders and epileptic seizures that are triggered by certain music. In this paper, we investigate the effects of 3 different genres of music in participants' Electrodermal Activity (EDA). Signals were recorded from 24 participants while they listened to 12 music stimuli. Various feature selection methods were applied to a number of features which were extracted from the signals. A simple neural network using Genetic Algorithm (GA) feature selection can reach as high as 96.8% accuracy in classifying 3 different music genres. Classification based on participants' subjective rating of emotion reaches 98.3% accuracy with the Statistical Dependency (SD) / Minimal Redundancy Maximum Relevance (MRMR) feature selection technique. This shows that human emotion has a strong correlation with different types of music. In the future this system can be used to distinguish music based on their positive of negative effect on human mental health.
KW - Classification
KW - Electro-dermal Activity
KW - Music Therapy
KW - Physiological Signals
UR - http://www.scopus.com/inward/record.url?scp=85073212679&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073212679&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8852318
DO - 10.1109/IJCNN.2019.8852318
M3 - Conference contribution
AN - SCOPUS:85073212679
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 July 2019 through 19 July 2019
ER -
