Mood Based Music Playlist Generator Using Convolutional Neural Network

Abstract

Abstract: Music is used in one’s everyday life to modulate, enhance, and plummet undesirable emotional states like stress, fatigue, or anxiety. Even though in today's modern world, with the ever-increasing advancement in the area of multimedia content and usage, various intuitive music players with various options have been developed, the user still has to manually browse through the list of songs and select songs that complement and suit his or her current mood. This paper aims to solve this issue of the manual finding of songs to suit one’s mood along with creating a high accuracy CNN model for Facial emotion recognition. Through the webcam, the emotional state can be deduced from facial expressions. To create a neural network model, the CNN classifier was used. This model is trained and tested using OpenCV to detect mood from facial expressions. A playlist will be generated through the system according to the mood predicted by the model. The model used for predicting the mood obtained an accuracy of 97.42% with 0.09 loss on the training data from the FER2013 Dataset. Keywords: Convolutional neural networks, Facial emotion recognition, FER2013, Music, OpenCV.