MUSIC RECOMMENDATION SYSTEM WITH EMOTION DETECTION (using DCNN model)

Abstract

Music has been a part of a life and some people spend hours creating a playlist. There are a lot of payable music recommendation available. A user’s emotion or mood can be detected by his/her facial expressions. These expressions can be derived from the live feed via system’s camera. Emotions are affective states which show an individual’s response to mental stimulus. We will present final development of our emotion based music recommendation system, focusing on the integration of facial recognition algorithms and music recommendation engine. The facial emotion recognition component utilized computer vision techniques and deep learning models to analyze facial expression and accurately identify the user’s emotional state. The music recommendation engine is designed to classify music and provide suggestion that align with the user’s current emotional state. The music classification system classify genre of music from its melfrequncy and analyze emotional tag of a music. The integration of facial algorithms with the music recommendation engine allows for a more seamless and intuitive user experience.