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One of the essential purposes of sound classification is to achieve similar/over classification ability of the human auditory system (HAS). A new dataset and a biologically inspired feature extraction function have been proposed to realize this aim. We have developed a highly accurate sound classification architecture using the proposed biological-inspired feature extraction function.

In this research, a new environmental sound classification (ESC) dataset has been collected as a testbed, and this dataset contains 5000 sounds with 50 classes. Moreover, the collected ESC sound dataset is balanced. A new hand-modeled sound classification model has been proposed to classify sounds of this dataset. This model consists of (i) feature generation using a new lateral geniculate nucleus pattern (LGNPat), statistical moments and discrete wavelet transform (DWT), (ii) loop …