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@article{182307,
        author = {Harshini Sanjay Kotgirwar and Sayali Anil Thak and Bharti Sanjay Shingade and Prerana Liladharrao Gawande and Sushant Sureshrao Morghade},
        title = {Implementation of Emotion Detection using Music Recommendation System with CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1819-1822},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182307},
        abstract = {This paper presents an intelligent music recommendation system driven by real-time facial emotion recognition.  Using a webcam, the system uses a Convolutional Neural Network (CNN) to classify emotions like happiness, sadness, anger, surprise, and neutrality based on the user's facial expressions. Based on the detected emotion, it recommends an appropriate music playlist to enhance or complement the user's mood.  Technologies including OpenCV, TensorFlow, and Python GUI libraries such as Tkinter and Pygame are used to implement the system.  The recommendation engine employs a hybrid filtering technique that combines content-based and collaborative filtering for improved accuracy. Experimental results show promising emotion detection accuracy and fast music suggestions, offering a smooth and emotionally intelligent user experience.},
        keywords = {CNN, emotion recognition, face detection, hybrid filtering, music recommendation, OpenCV, TensorFlow, user interface.},
        month = {July},
        }