SKLBP14: A new textural environmental sound classification model based on a square-kernelled local binary pattern

Abstract

Nowadays, the forward-forward (FF) algorithm is very popular in the machine learning society, and it uses a square-based activation function. In this research, we inspired the FF algorithm and presented a new kernel for a local binary pattern named square-kernelled local binary pattern (SKLBP). By deploying the proposed one-dimensional SKLBP, a new feature engineering model has been presented. To measure the classification ability of the proposed SKLBP-based model, we have collected a new textural environmental sound classification (ESC) dataset. The collected dataset is a balanced dataset, and it contains 15 classes. There are 100 sounds in each class. Our proposed model has mimicked the deep learning structure. Therefore, it uses multileveled feature extraction methodology by using discrete wavelet transform. The features generated have been considered as input for the iterative feature selector. The chosen feature vector has been utilized as input of the k nearest neighbor classifier. The proposed SKLBP-based signal classification model reached 94% classification accuracy. In this aspect, we contributed to the ESC methodology by collecting the new textural ESC dataset and proposing the SKLBP-based ESC model.