EMOTION BASED MUSIC RECOMMEDENTION SYSTEM

Abstract

Abstract: Emotion-based music recommendation is a challenging task that has attracted significant research attention in recent years. This paper proposes a novel approach to emotion-based music recommendation using Support Vector Machines (SVMs). Our approach uses images as inputs instead of pronouns to capture the emotional state of the user more accurately. We collected a dataset of images associated with different emotional states and extracted features using Convolutional Neural Networks (CNNs). Then, we trained an SVM model to predict the emotional state of the user based on the input image. Finally, we used the output of the SVM model to recommend music tracks that are most suitable for the user’s emotional state. Our experiments on a large dataset show that our approach outperforms existing methods in terms of accuracy and user satisfaction. The proposed approach provides a promising direction for emotion-based music recommendation systems that can enhance the user experience by providing personalized music recommendations based on their emotional state. Keywords- Emotion Detection, Face Recognition, Music API.