Abstract
Abstract: Music is a fantastic way for people to express themselves as well as a good source of enjoyment for music loversand listeners. Besides, relaxing music is an effective manner for eliciting strong feelings and starting a quiet communication. With technological advancements, the number of artists, their music, and music listeners is increasing, which brings up the consequence of manually searching and picking music. This study offers a system that uses facial expressions at real time of a user to assess the his/her mood( Emotion detection Model), product of which is also combined with mapped music from the music dataset to generate a user-specific music playlist( music recommendation model). Convolutional neural network is used to classify the users sentiments in 7 different kinds with an accurateness rate of 94 percent, therefore satisfying the effective aim of the study.
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