The approach of using audio datasets to solve the problems revolving around land use patterns has gained decent amount of research exposure in the recent times. Although being a blooming domain in the field of Artificial Intelligence and Data Analysis, it would not be wrong to declare that a significant amount of journey is yet to be covered. In this paper, the project conducted to classify some pre-defined acoustic scene is discussed and explained.For the system designed INPUT : ambient audio recording of a certain scene OUTPUT : class to which the scene belongs The approach used not only challenges some of the fundamental mathematical techniques used so far in early experiments of the same trend, but also it introduces new scopes and new horizons for interesting results. The physics governing spectrograms has been optimized in the project along with exploring how it handles the intense requirements of the problem in 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, audio transforms principle were also tried and explored, and indeed the insights gained were used constructively in the later stages of 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, but they are surely potent enough to produce novel solutions in various different domains of diverse backgrounds.
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