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The approach used not only challenges some of the fundamental mathematicaltechniques used so far in early experiments of the same trend but alsointroduces new scopes and new horizons for interesting results. The physicsgoverning spectrograms have been optimized in the project along with exploringhow it handles the intense requirements of the problem at hand. Majorcontributions and developments brought under the light, through this projectinvolve using better mathematical techniques and problem-specific machinelearning methods. Improvised data analysis and data augmentation for audiodatasets like frequency masking and random frequency-time stretching are usedin the project and hence are explained in this paper. In the used methodology,the audio transforms principle were also tried and explored, and indeed theinsights 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, thepotential scopes and upcoming research openings in both short and long termtunnel of time has been presented. Although much of the results gained aredomain-specific as of now, they are surely potent enough to produce novelsolutions in various different domains of diverse backgrounds.

Acoustic scene classification using auditory datasets