Emotion-Driven Soundtrack Selector using AI

Abstract: The work portrayed the advancement of emotion-based Music Player, which is a web application implied for a wide range of clients, explicitly the music sweethearts. With the convenience of music players and other streaming apps, people can listen to music anytime, anywhere, and engage in various activities such as traveling, sports, or daily routines. The advancement of mobile networks and digital multimedia technologies has made digital music the most sought-after consumer content by young people. Moreover, listening to the appropriate music at the right time has the potential to enhance mental well-being. Human inclination assumes an indispensable part as of late. Emotion depends on human sentiments which can be both communicated or not. Emotion communicates the human's singular way of behaving which can be in various forms. The target of this project is to separate elements from the human face and recognize the emotion and play music as per the emotion identified. Be that as it may, many existing/strategies utilize past information to recommend music and different calculations utilized no Facial articulations are caught by a nearby catching gadget or an inbuilt web camera. Here we utilize a calculation for the acknowledgment of the component from the emotion we caught. Many music recommendation systems use content-based or collaborative-based recommendations. However, the choice of music for a user is not only based on his historical preference of the user. But also based on the mood of that user. This project proposes an emotion-based music recommendation that detects the emotion of a user from the webcam and plays the songs based on the user’s mood. In particular, the emotion of a user is classified and predicted the emotion and automatically redirects to the music page. This emotional information is stored in welldefined arrays. Thus, the model can predict the emotion and accuracy can be increased using these data.