Automatic music mood detection using transfer learning and multilayer perceptron

Abstract

This paper proposes an automatic mood detection of music with a composition of transfer learning and multilayer. The five layered convolutional neural network pre-trained on Million Song dataset is used to extract the features from EmoMusic dataset. We obtain a set of features from the different five layers, which is fed into multilayer perceptron (MLP)-based regression. Through the regression network we estimate the mood of music on Thayer's two-dimensional emotion space, which consists of the axes corresponding to arousal and valence. Because the EmoMusic dataset does not provide enough number of data for training, we augment the data by time stretching to make it tripled. We perform the experiment with the augmented data as well as the original EmoMusic dataset. Box and whisker plot along with the mean of 10-fold cross-validation has been used for evaluating the proposed mood detection. In terms of the percentage of R2 score for measure of accuracy, the proposed MLP shows state-of-the-art estimates for the augmented EmoMusic dataset.