Exploring Music-Mental Health Dynamics Through Hierarchical Temporal Memory-based VAE-GAN Algorithm

Abstract:
Music has been known to have a profound effect on mental health for generations. This study focuses on finding patterns, relationships, and possible causal relationships between musical characteristics and mental health. The study employs a combination of a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) model integrated with a Hierarchical Temporal Memory (HTM) model to identify these relationship patterns. In this study, the VAE-GAN allows for a multitude of musical representations while the HTM model can develop a spatial-temporal hierarchy of patterns in music and throughout the data. This unique approach will provide the HTM-based VAE-GAN model to uncover hidden relationships between changes in musical features and mental health features. The new approach provides a pathway to better understanding relationships between music and mental health.