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@article{164818,
        author = {Anubhav Kumar and Anshu Sharma and Abhishek and Aditya Rajpoot and Aditya Singh and Ayushi Chaudhary},
        title = {Mood Based Music Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2262-2268},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164818},
        abstract = {Psychological research has demonstrated that music effectively relieves stress, elevates mood, and triggers the release of neurochemicals such as oxytocin, serotonin, and dopamine. Consequently, music is widely utilized in therapeutic settings and rehabilitation centers for various disorders. As global mental health issues escalate, it is increasingly critical to address these concerns with innovative solutions. Existing music recommendation systems are inadequate in holistically addressing user needs, with few offerings mood-based recommendations and even fewer integrating therapeutic benefits tailored to specific situations. Given the widespread reliance on music for enhancing cognition, memory, and sleep quality, while reducing anxiety, pain, and blood pressure, there is a pressing need for a comprehensive product that maximizes these benefits. Our proposed system introduces a mood-based music player that performs real-time mood detection and recommends songs accordingly, enhancing traditional music player apps with this additional feature. This system aims to analyze user images, predict emotional expressions, and suggest suitable music, thereby improving user satisfaction and overall mental health.},
        keywords = {Face Recognition, Image Processing, Computer Vision, Emotion Detection, Music, Mood Detection, Content-Based Filtering, Mental Health, Machine Learning.},
        month = {},
        }