Swetha Kambham ¹, Hubert A, Johnson ¹ and Sai Prathap Reddy Kambham ², ¹ Montclair State University, USA, ² ERP Health LLC, USA

As artificial intelligence becomes more and more ingrained in daily life, we present a novel system that uses deep learning for music recommendation and emotion-based detection. Through the use of facial recognition and the DeepFace framework, our method analyses human emotions in real-time and then plays music that reflects the mood it has discovered. The system uses a webcam to take pictures, analyses the most common facial expression, and then pulls a playlist from local storage that corresponds to the mood it has detected. An engaging and customised experience is ensured by allowing users to manually change the song selection via a dropdown menu or navigation buttons. By continuously looping over the playlist, the technology guarantees continuity. The objective of our system is to improve emotional well-being through music therapy by offering a responsive and automated music-selection experience.

Music Recommendation System, Deep Learning, Emotion Recognition, Facial Expression Analysis, Real-Time Emotion Detection, Affective Computing Human-Computer Interaction (HCI), Artificial Intelligence in Music Therapy, Computer Vision, Automated, Music Selection, Sentiment Analysis.