Deciphering Emotional Responses to Music: A Fusion of Psychophysiological Data Analysis and Bi-LSTM Predictive Modeling
| dc.contributor.author | Mahat, Maheep | en |
| dc.contributor.committeechair | Gracanin, Denis | en |
| dc.contributor.committeemember | Knapp, Richard Benjamin | en |
| dc.contributor.committeemember | Wenskovitch, John Edward | en |
| dc.contributor.department | Computer Science and#38; Applications | en |
| dc.date.accessioned | 2024-06-11T08:00:29Z | en |
| dc.date.available | 2024-06-11T08:00:29Z | en |
| dc.date.issued | 2024-06-10 | en |
| dc.description.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. | en |
| dc.description.abstractgeneral | We explored how people's bodies react over time when they listen to music. We used a large collection of data called the ``Emotion in Motion'' dataset, which has information from people in seven countries who listened to three different music pieces. To understand this data, we used special tools that help detect patterns and changes in how the body responds. During the music sessions, the participant's skin's electrical activity and the amount of oxygen in the blood were recorded, which can give clues about emotional reactions. People also shared their feelings about the music. To make it easier to see and understand all this information together, we created a new web platform that simulates the experiment in real-time. This work aims to help us better understand the deep connection between music and human emotions. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41056 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/119377 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Psypchophysiological Data | en |
| dc.subject | EDA | en |
| dc.subject | POX | en |
| dc.subject | Dynamic Time Warping | en |
| dc.subject | LSTM | en |
| dc.subject | Bi-LSTM | en |
| dc.title | Deciphering Emotional Responses to Music: A Fusion of Psychophysiological Data Analysis and Bi-LSTM Predictive Modeling | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science & Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |