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@article{206746,
        author = {B Shraddha Shetty and Dr Jithendra P R Nayak and Chandana B and Lavanya A and Kavya},
        title = {Emotion Based Music Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {284-291},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206746},
        abstract = {Music has a profound impact on human emotions, often prompting individuals to select songs that either reflect or alter their current mood. Most current music recommendation platforms depend on factors like genre, popularity, or past listening habits, but they frequently overlook the listener's emotional state. This project introduces an Intelligent Emotion-Based Music Recommendation System that identifies emotions from speech and suggests appropriate music in response. The system processes voice input or uploaded audio using techniques such as noise reduction, normalization, and feature extraction, including MFCCs and spectrograms. A deep learning model combining CNN and RNN architectures is employed to classify emotions such as happy, sad, angry, calm, and neutral. Based on the detected emotion, the system automatically generates personalized music recommendations by integrating APIs like Spotify and YouTube. This approach improves user experience by adapting to real-time emotional changes and supports mental well-being through mood-aligned suggestions. The system uses reinforcement learning to further personalize recommendations based on user feedback, and real-time playback via APIs ensures compatibility across platforms. By focusing on emotional context, the system delivers adaptive, emotion-aware music experiences tailored to individual users},
        keywords = {Speech Emotion Recognition, Music Recommendation System, Deep Learning, MFCC, CNN-RNN, Audio Processing.},
        month = {July},
        }