Mood Music Recommendation Using Emotion Detection

Abstract

Abstract: The proposed mood music recommendation system leverages the power of deep learning and emotion detection technology to provide personalized and dynamic music recommendations to users. The system uses various modalities such as facial expressions, speech patterns, and voice tone to detect the user's emotional state. The system then recommends songs and playlists that are aligned with the user's mood, thereby enhancing their overall music listening experience. One of the key advantages of the proposed system is its adaptability to changes in the user's emotional state. As the user's mood changes, the system can dynamically adjust the music recommendations to ensure that the user is constantly provided with music that is appropriate to their current emotional state. This adaptability is crucial as emotions can be unpredictable and constantly changing. By providing music that is tailored to the user's current mood, the system can help them manage their emotions and improve their well-being Overall, the proposed system has the potential to revolutionize the music streaming industry by providing users with a highly personalized and dynamic music listening experience. The use of emotion detection technology and deep learning algorithms can help users manage their emotions and enhance their overall listening experience, thereby contributing to their overall well-being. The proposed system represents an exciting step towards the development of intelligent and adaptive music recommendation systems.