  Music is often considered as the language of emotions. It has long been known
to elicit emotions in human being and thus categorizing music based on the type
of 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 ambiguity
associated with ICM. The fact that a single musical performance can evoke a
variety of emotional response in the audience is implicit to the nature of ICM
renditions. With the rapid advancements in the field of Deep Learning, this
Music Emotion Recognition (MER) task is becoming more and more relevant and
robust, 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 dataset
called JUMusEmoDB which presently has 400 audio clips (30 seconds each) where
200 clips correspond to happy emotions and the remaining 200 clips correspond
to sad emotion. For supervised classification purposes, we have used 4 existing
deep Convolutional Neural Network (CNN) based architectures (resnet18,
mobilenet v2.0, squeezenet v1.0 and vgg16) on corresponding music spectrograms
of the 2000 sub-clips (where every clip was segmented into 5 sub-clips of about
5 seconds each) which contain both time as well as frequency domain
information. The initial results are quite inspiring, and we look forward to
setting the baseline values for the dataset using this architecture. This type
of CNN based classification algorithm using a rich corpus of Indian Classical
Music is unique even in the global perspective and can be replicated in other
modalities of music also. This dataset is still under development and we plan
to include more data containing other emotional features as well. We plan to
make the dataset publicly available soon.
