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

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Mahat, Maheep

Chair dc:contributor.committeechair

• Gracanin, Denis

Committee members dc:contributor.committeemember

• Knapp, Richard Benjamin
• Wenskovitch, John Edward

• Psypchophysiological Data
• EDA
• POX
• Dynamic Time Warping
• LSTM
• Bi-LSTM

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41056

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119377