SKLBP14: A new textural environmental sound classification model based on a squarekernelled local binary pattern

Authors : Arif Metehan YILDIZ, Mehmet Veysel GUN, Kubra YILDIRIM, Tugce KELES, Sengul DOGAN, Turker TUNCER, U Rajendra ACHARYA

Pages : 46-54

Doi:10.5505/fujece.2023.03521

View : 31 | Download : 22

Publication Date : 2023-06-14

Article Type : Research Paper

Abstract :Nowadays, the forward-forward insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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.
Keywords : Textural ESC dataset, Square kernelled local binary pattern, Signal classification, Advanced signal processing, Textural feature extraction