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Music is often considered as the language of emotions. It has long been knownto elicit emotions in human being and thus categorizing music based on the typeof emotions they induce in human being is a very intriguing topic of research.When the task comes to classify emotions elicited by Indian Classical Music(ICM), it becomes much more challenging because of the inherent ambiguityassociated with ICM. The fact that a single musical performance can evoke avariety of emotional response in the audience is implicit to the nature of ICMrenditions. With the rapid advancements in the field of Deep Learning, thisMusic Emotion Recognition (MER) task is becoming more and more relevant androbust, hence can be applied to one of the most challenging test case i.e.classifying emotions elicited from ICM. In this paper we present a new datasetcalled JUMusEmoDB which presently has 400 audio clips (30 seconds each) where200 clips correspond to happy emotions and the remaining 200 clips correspondto sad emotion. For supervised classification purposes, we have used 4 existingdeep Convolutional Neural Network (CNN) based architectures (resnet18,mobilenet v2.0, squeezenet v1.0 and vgg16) on corresponding music spectrogramsof the 2000 sub-clips (where every clip was segmented into 5 sub-clips of about5 seconds each) which contain both time as well as frequency domaininformation. The initial results are quite inspiring, and we look forward tosetting the baseline values for the dataset using this architecture. This typeof CNN based classification algorithm using a rich corpus of Indian ClassicalMusic is unique even in the global perspective and can be replicated in othermodalities of music also. This dataset is still under development and we planto include more data containing other emotional features as well. We plan tomake the dataset publicly available soon.

Neural Network architectures to classify emotions in Indian Classical Music