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

This paper presents a comprehensive study on the utilization of the “Emotion in Motion” database, the world’s largest repository of psychophysiological data elicited by musical stimuli. Our work is centered around three key endeavors. First, we developed an interactive online platform to visualize and engage with the database, providing a user-friendly interface for researchers and enthusiasts alike to explore the intricate relationships between music and physiological responses. This platform stands as a significant contribution to the field, offering novel ways to interact with and interpret the complex data. Second, we conducted an in-depth correlation analysis of the physiological signals using Dynamic Time Warping within the database. By categorizing the data into two main genres of music — classical and modern — and further subdividing them into three age-specific groups, we gleaned valuable insights into how different demographics respond to varied musical styles. This segmentation illuminated the nuanced interplay between age, music genre, and physiological reactions, contributing to a deeper understanding of music’s emotional impact. Finally, we developed a predictive model using Long Short-Term Memory (LSTM) networks, capable of processing Electrodermal Activity (EDA) and Pulse Oximetry (POX) signals. Our model adopts a sequence-to-vector prediction approach, effectively fore-casting seven distinct emotional attributes in response to musical stimuli. This LSTM-based model represents a significant advancement in predictive analytics for music-induced emotions, showcasing the potential of machine learning in deciphering complex human responses to art. Our work not only provides novel tools and insights for analyzing psychophysiological data but also opens new avenues for understanding the emotional power of music across different demographics, ultimately bridging gaps between music psychology, physiology, and computational analysis.