Development of music emotion classification system using convolution neural network

Music emotion classification (MEC) is the multidisciplinary research area that is related to perceive the emotions from the songs and label the songs with particular emotion classes. MEC systems (MECS) extract the features from the songs and then the songs are categorized on the basis of emotions by comparing their features. In this paper an MECS has been proposed that makes use of Convolutional Neural Network (CNN) by converting the music to their visual representation known as spectrograms. By using CNN extraction of specific features of music signals is not necessarily required to classify the songs. In this work two MECS are trained and tested by using Hindi database by using CNN and third MECS system is developed by using SVM. In first MECS spectrograms are obtained by using hamming windows of size 2048 and noverlap factor of 1024 and in second MECS spectrograms are obtained by using hamming windows of size 1024 and noverlap factor of 512. The three combinations of CNN layers are used in order to classify the songs in four, eight and sixteen classes on the basis of emotional tags. The performance of MECS design is analyzed on the basis of training accuracy, validation accuracy, training loss and validation loss. Results show that the two MECS systems developed by CNN has better accuracy and less loss than the third MECS system modeled by SVM.