Melodious Micro-frissons: Detecting Music Genres From Skin Response

• Published in: 2019 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
• Main Authors: Rahman, Jessica Sharmin, Gedeon, Tom, Caldwell, Sabrina, Jones, Richard, Hossain, Md Zakir, Zhu, Xuanying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Australia; Classification; Computer science; Electro-dermal Activity; Epilepsy; Medical treatment; Multiple signal classification; Music; Music Therapy; Physiological Signals; Physiology
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2019.8852318

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.