MUSIC RECOMMENDATION SYSTEM BASED ON REALTIME USER EMOTIONS

Abstract: The increasing volume of digital music content has led to a growing demand for personalized music recommendation systems that can understand and cater to individual preferences. This paper proposes an emotion-based music recommendation system leveraging machine learning techniques and implemented using Python technology. The system aims to enhance user satisfaction and engagement by recommending music tracks based on emotional context, providing a more immersive and personalized listening experience. Key components of the system include a robust data preprocessing pipeline, feature extraction from audio signals, and the development of machine learning models trained on emotion-labeled datasets. Python libraries such as Pandas, NumPy, and Scikit-Learn are utilized for data manipulation, feature extraction, and model training. The system employs state-of-the-art machine learning algorithms, such as deep neural networks, to extract high-level emotional features from audio data. Evaluation of the proposed system involves assessing its recommendation accuracy, user satisfaction, and the system's ability to adapt to dynamic changes in user preferences and emotional states. The results are obtained through user studies and objective metrics, demonstrating the effectiveness and efficiency of the implemented emotion-based music recommendation system.

Keywords: Machine Learning, Python, Emotion Recognition, Music Suggestions.