  The approach used not only challenges some of the fundamental mathematical
techniques used so far in early experiments of the same trend but also
introduces new scopes and new horizons for interesting results. The physics
governing spectrograms have been optimized in the project along with exploring
how it handles the intense requirements of the problem at hand. Major
contributions and developments brought under the light, through this project
involve using better mathematical techniques and problem-specific machine
learning methods. Improvised data analysis and data augmentation for audio
datasets like frequency masking and random frequency-time stretching are used
in the project and hence are explained in this paper. In the used methodology,
the audio transforms principle were also tried and explored, and indeed the
insights gained were used constructively in the later stages of the project.
Using a deep learning principle is surely one of them. Also, in this paper, the
potential scopes and upcoming research openings in both short and long term
tunnel of time has been presented. Although much of the results gained are
domain-specific as of now, they are surely potent enough to produce novel
solutions in various different domains of diverse backgrounds.
