Emotion Monitoring Music Player Using Convolution Neural Network and Deep Learning

Listening to different types of music of one’s individual taste and mood is something that people desire for. On an average person spends most of his time on listening to music. Music has a high impact on an individual’s brain activity. In the recent events there are plenty of music in different language and style that people are getting confused to select the exact music that suit’s that person’s particular emotion at time. In the existing system, the algorithms in use are relatively slow, less accurate and sometimes require use of additional hardware like EEG or sensors. Emotions of a person are easy to identify using the facial expressions. My model is based on identifying the person’s emotion or mood to play the relevant songs for the emotion. In this project we use Haar Cascade algorithm to identify the facial expressions, we are also using the real time data set to identify the emotion of the user. My project will be voice activated, easy to access and make sure that it doesn’t affect the individual’s daily life. It also monitors the emotional status of the person at the end of every song’s starting and ending to have a medical dataset of the individual’s emotions status