“A Real Time Sentiment Adaptive Music Recommendation System using YoloV8”

Abstract: The discussion regarding music recommendation continues to be mentioned repeatedly since this may strengthen consumer interactions through multiple manners, especially psychic and personal memorable ones. Our project highlights a new development, that includes YOLOv8’s deep learning-driven gesture understanding. Applying a live webcam, an individual’s movements get recorded during actual time and are capable of being grouped into happy, sad, angry, neutral or surprised. While a particular mood becomes apparent, an arrangement of soundtracks will be generated according to the underlying feelings, which at first will be chosen by the database of songs that has been previously established. In light of its faster interpretation rate in addition with characteristic extraction skill, the YOLOv8 prototype has been employed for this research to figure out the movement of face. The result of the observation shows that the implemented system is efficient in identifying facial emotions with high accuracy and recommending music that aligns the current mood of a user. This study emphasizes the potential of integrating cognitive computing and Data Science methods to create intelligent multimedia applications that adapt in real-time to an individual's emotional condition.

Keywords: Emotion classification, OpenCV, music suggestion, Facial Extraction, YOLOv8, Real time capturing images.