Neural Network based Adaptive Framework for Music Emotion Regulation

Music plays a profound role in influencing and regulating human emotions, making it a valuable tool in therapeutic, educational, and entertainment settings. Traditional methods of music emotion regulation rely on static classifications or user-defined playlists, which often fail to adapt to dynamic emotional states. This research presents a neural network-based framework for music emotion regulation that utilizes real-time emotional feedback and music features to personalize listening experiences. By leveraging Machine learning and Deep learning techniques, the system analyzes both audio signals and user physiological responses to predict emotional states and suggest music accordingly. The proposed model is trained on multimodal datasets combining emotional annotations with acoustic features and biometric signals. Experimental results demonstrate that the system can achieve an accuracy of 96.7% in predicting user emotions and significantly enhance emotional well-being through adaptive music selection. This study provides a foundation for intelligent music therapy systems, emotional wellness applications, and AI-driven content recommendation engines.