Melodious micro-frissons: detecting music genres from skin response

Jessica Sharmin Rahman; Tom Gedeon; Sabrina Caldwell; Richard Jones; Md Zakir Hossain; Xuanying Zhu

doi:10.1109/IJCNN.2019.8852318

Melodious micro-frissons: detecting music genres from skin response

Jessica Sharmin Rahman, Tom Gedeon, Sabrina Caldwell, Richard Jones, Md Zakir Hossain, Xuanying Zhu

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2019 International Joint Conference on Neural Networks, IJCNN 2019
Place of Publication	Budapest
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781728119854
ISBN (Print)	9781728119861
DOIs	https://doi.org/10.1109/IJCNN.2019.8852318
Publication status	Published - Jul 2019
Externally published	Yes
Event	2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Publisher	IEEE
Volume	2019-July
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/Territory	Hungary
City	Budapest
Period	14/07/19 → 19/07/19

Keywords

Classification
Electro-dermal Activity
Music Therapy
Physiological Signals

Access to Document

10.1109/IJCNN.2019.8852318

Cite this

Rahman, J. S., Gedeon, T., Caldwell, S., Jones, R., Hossain, M. Z., & Zhu, X. (2019). Melodious micro-frissons: detecting music genres from skin response. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 (pp. 1-8). Article 8852318 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IJCNN.2019.8852318

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title = "Melodious micro-frissons: detecting music genres from skin response",

abstract = "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.",

keywords = "Classification, Electro-dermal Activity, Music Therapy, Physiological Signals",

author = "Rahman, \{Jessica Sharmin\} and Tom Gedeon and Sabrina Caldwell and Richard Jones and Hossain, \{Md Zakir\} and Xuanying Zhu",

year = "2019",

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language = "English",

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Rahman, JS, Gedeon, T, Caldwell, S, Jones, R, Hossain, MZ & Zhu, X 2019, Melodious micro-frissons: detecting music genres from skin response. in 2019 International Joint Conference on Neural Networks, IJCNN 2019., 8852318, Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Institute of Electrical and Electronics Engineers (IEEE), Budapest, pp. 1-8, 2019 International Joint Conference on Neural Networks, IJCNN 2019, Budapest, Hungary, 14/07/19. https://doi.org/10.1109/IJCNN.2019.8852318

Melodious micro-frissons: detecting music genres from skin response. / Rahman, Jessica Sharmin; Gedeon, Tom; Caldwell, Sabrina et al.
2019 International Joint Conference on Neural Networks, IJCNN 2019. Budapest: Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 1-8 8852318 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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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.

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ER -

Rahman JS, Gedeon T, Caldwell S, Jones R, Hossain MZ, Zhu X. Melodious micro-frissons: detecting music genres from skin response. In 2019 International Joint Conference on Neural Networks, IJCNN 2019. Budapest: Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 1-8. 8852318. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2019.8852318

Melodious micro-frissons: detecting music genres from skin response

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