Emotion Classification of Songs Using Deep Learning

The sentiment prediction of songs will have a vast quantity of applications within the current era, like the Music recommendation system for request, choosing the music for public gatherings like procession or restaurants to enhance the emotional standing of a personal or cluster, seemingly folks and customers. A song is one way to say something indirectly and one of the most adventurous parts is that the one song could express more than one emotion. We can use songs to apologize, congratulate, express happiness or unhappiness, etc. Emotion is a vital part of daily life: it affects choice-making, conception, human interdependence, and human understanding. There are units of positive emotions and negative emotions; positive emotions are units a lot related to human health also as work potency, whereas negative emotions could cause health issues. In this research paper, we are going to use Deep Learning Frameworks, which are mostly used for classification techniques like Multilayer Perceptron (MLP), Gated Recurrent Units(GRU), Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). In this paper, the dataset operated is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)[1] for the enactment of audio categorization. As a result, we obtained graphs for each Deep Learning framework that is for training and validation loss, training and validation accuracy. After implementation, we got the highest accuracy for CNN that is 92.71% and the least accuracy for MLP that is 52.60% and for LSTM and GRU it lies between the range of 63% - 66%.