Emotion Based Music Recommendation System Using LSTM - CNN Architecture

Abstract

In the current era of media and technology, music information retrieval techniques have made progress in the past years but the music recommendation system is still at a rudimentary stage. However personalized music recommendation is quotidian, but recommending songs based on emotions is still an uphill battle. Music has greatly influenced the human brain and helps dispense an exhilarating and frivolous state of mind because it helps us work more effectively. Recommending songs based on emotions will comfort the listener by suggesting music in keeping with the listeners' pervading mental and physical state. Hence, Natural Language Processing and Deep Learning technologies made it possible for machines to read and interpret emotions through texts by recognizing patterns and finding correlations. In this paper, various deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural network (CNN), CNN-LSTM, and LSTM-CNN Architectures were collated for detecting emotions such as angry, happy, love, and sad, the best model was integrated into the application. To enhance the application, a CNN model was used to detect emotions through facial expressions. The application takes text input from the user or a facial expression input. Depending upon the emotion detected, it recommends the user songs and playlists.