EmoTunes: Personalized Playlists for Every Mood

With the rise of music streaming platforms, users now have access to an unprecedented amount of music worldwide.However, this abundance often overwhelms users, leading to decision paralysis and difficulty in finding music that aligns with their emotional state.To address this challenge, researchers and developers are exploring the integration of emotion recognition technology into music recommendation systems to improve user experience and engagement.This paper examines the importance of incorporating emotions into music recommendation systems and explores various approaches to emotion recognition, including physiological signals, audio features, and user-provided data.It also investigates different recommendation algorithms used in emotion-based systems, such as content-based filtering approaches.Our proposed system involves the creation of an emotion-based music player that performs realtime mood detection and suggests songs based on the detected mood.The system analyses the user's image, predicts their expression, and recommends songs suitable for the detected mood.This paper presents a thorough review of existing emotion-based music recommendation systems, focusing on their methodologies, strengths, limitations, and future directions.