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@article{171558,
        author = {A.ROSHINI and G.SRIJA REDDY and G.JASHWANTH SHIVA and G.PAWAN KALYAN},
        title = {SENTIMENT BASED ENHANCED MUSIC RECOMMENDATION SYSTEM USING CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {8},
        pages = {244-247},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171558},
        abstract = {This project carries out the real-time emotion detection with music recommendation using facial expression analysis. In this system, the system classifies facial expressions into seven separate emotions: Angry, Disgusted, Fear, Happy, Sad, Surprise, and Neutral. It is done by implementing a pre-trained Convolutional Neural Network (CNN). Live video feed from a webcam has been processed in order to carry out face detection and emotion prediction based on the detected facial features using OpenCV's Haar Cascade classifier. Once the system detects the state of emotion, it selects a song that has a connection with the emotional state identified on a pre-listed list tied with each emotional state, retrieves it from YouTube using a youtubesearchpython library, and allows the system to play the song in a web browser. In this manner, it provides for an automatic response through the form of music pertaining to the emotion identified by the system. Its applications combine aspects from machine learning, computer vision, and music recommendation toward potential applications in interactive entertainment, personal media experience, and emotional well-being tools.},
        keywords = {Emotion detection, music recommendation, Convolutional Neural Network (CNN), YouTube integration.},
        month = {December},
        }