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@article{206820,
        author = {Shilpa Bhandari and Dr. Soumya},
        title = {Closed-Loop Emotion-Regulation Through Dynamic Music Generation: An AI Framework for Stress and Mood Adaptation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {577-580},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206820},
        abstract = {The growing levels of stress and mental health challenges in modern society have increased the need for intelligent and personalized emotional regulation tools. This paper presents a closed-loop artificial intelligence framework that dynamically generates music to help regulate users’ emotional states in real time. The proposed system combines emotion recognition techniques with generative music models to create personalized soundscapes that support stress reduction and mood stabilization. The framework continuously monitors emotional signals using physiological and behavioral data obtained from wearable sensors and user interactions. Based on this information, the system adapts musical attributes such as tempo, harmony, rhythm, and timbre to influence the listener’s emotional state. Machine learning models analyze biometric indicators including heart rate variability, facial expressions, and vocal characteristics to estimate emotional conditions, which are widely used in affective computing systems [2], [7].
A generative music engine then produces adaptive musical compositions tailored to the detected mood using deep learning–based music generation models [3], [16]. In addition, the architecture incorporates reinforcement learning to refine music generation strategies based on user responses, allowing the system to gradually improve its effectiveness over time [10]. Experimental evaluation indicates that dynamically generated AI music provides better relaxation and mood stabilization compared to conventional static playlists. The proposed framework offers a scalable and personalized solution for digital mental wellbeing applications such as therapeutic support systems, meditation platforms, and stress-management tools.},
        keywords = {Emotion Recognition, Generative AI, Music Generation, Reinforcement Learning, Stress Detection, Human-AI Interaction, Adaptive Music Systems.},
        month = {July},
        }