EMUSIC USING SUPPORT VECTOR MACHINE LEARNING ALGORITHM

Abstract

The emotion or mood of a user can be detected by their facial expressions. Those expressions can be extracted from the live feed through the system’s camera. Machine learning provides various techniques, one of which is detection of facial expression. It connects us across markets, aeons, backgrounds, dialects, political views, and financial status. Nowadays, music applications and other streaming services are of high demand and are sought by many people not restricted to ages as there are a remarkable and rapid evolution of multimedia, digital music, and cellular networks. Most of the people use music for their mood regulation, increase energy level, and more specifically to change their unpleasant mood or reduce tension. In addition to it, by tuning in to the right type of music at the apparent time may refine your mental health. Thus, human emotions or mood have a intense bond with music. Here, in this project, we propose an efficient solution to meet the people needs in music by live feed and Support Vector Machine learning algorithms.