Recognition of emotion in music based on deep convolutional neural network

In the domain of music information retrieval, emotion based classification is an active area of research. Emotion being a perceptual and subjective concept, the task is quite challenging. It is very difficult to design signal based descriptors to represent emotions. In this work deep leaning network is proposed and experiment is done with benchmark datasets namely, Soundtracks, Bi-Modal and MER_taffc. Experiment has also been done with hand crafted descriptor consisting of different time domain and spectral features, linear predictive coding and MFCC based features. Different classifiers like, neural network, support vector machine and random forest are tried. Although the combined feature set with neural network provides an optimal result for the datasets, but in general the performance of such approaches is limited. It is difficult to obtain a consistent feature set that works across the classifier and datasets. To get rid of the issue of feature design, deep learning based approach is followed. A convolutional neural network built around VGGNet and a novel post-processing technique are proposed. Proposed methodology provides substantial improvement of performance for the datasets. Comparison with other reported works on three different datasets also establishes the superiority of the proposed methodology. The improvement in performance has been substantiated by Z test.