Deciphering Emotional Responses to Music: A Fusion of Psychophysiological Data Analysis and Bi-LSTM Predictive Modeling

Abstract

This research explores the temporal patterns of psychophysiological responses to musical excerpts by analyzing the expansive Emotion in Motion dataset, the most comprehensive of its kind. Utilizing the Dynamic Time Warping and T-test analysis techniques, we examined data from participants across seven countries who listened to three distinct musical pieces. During these listening sessions, Electrodermal Activity (EDA) and Pulse Oximetry (POX) readings were collected, complemented by qualitative feedback from the participants. Our analysis focused on detecting recurring patterns and extracting meaningful insights from the data. In addition to this, we compare several Deep Neural Networks to find the one that is best suited for prediction of emotional attributes with EDA and POX signals as input. To further facilitate a comprehensive visualization and analysis of the EDA, POX, and audio signals, we developed a dedicated platform, which features a coordinated multiple view interface, as an integral part of this work.