An automated Covid-19 respiratory sound classification method based on novel local symmetric Euclidean distance pattern and ReliefF iterative MRMR feature selector

Abstract

Covid-19 is a new variety of coronavirus that affects millions of people around the world. This virus infected millions of people and hundreds of thousands of people have passed away. Due to the panic caused by Covid-19, recently several researchers have tried to understand and to propose a solution to Covid-19 problem. Especially, researches in machine learning (ML) have been proposed to detect Covid-19 by using X-ray images. In this study, 10 classes of respiratory sounds, including respiratory sounds diagnosed with Covid-19 disease, were collected and ML methods were used to tackle this problem. The proposed respiratory sound classification method has been proposed in this study from feature generation network through hybrid and iterative feature selection to classification phases. A novel multileveled feature generating network is presented by gathering multilevel one-dimensional wavelet transform and a novel local symmetric Euclidean distance pattern (LSEDP). An automated hybrid feature selection method is proposed using ReliefF and ReliefF Iterative Maximum Relevancy Minimum Redundancy (RIMRMR) to select the optimal number of features. Four known classifiers were used to test the capability of our approach for lung disease detection in respiratory sounds. K nearest neighbors (kNN) method has achieved an accuracy of 91.02%.

A Gunshot Recognition Method Based on Multi-Scale Spectrum Shift Module

“…Hidden Markov model (HMM) [17] 5 gun models and 46 gunshots 95.65% 96.5% Convolutional neural networks [10] 59 gun models and 3655 gunshots 90% 93.2% Relief feature selector [18] 8 gun models with 851 gunshots 94.48% 95.1% Transfer learning [11] 18 gun models with 6000 gunshots 78.2% 83.2%…”

Towards an Indoor Gunshot Detection and Notification System Using Deep Learning

“…In the developed dataset, each class has 335,000 samples. The gunshot sounds were collected from different online sources such as the BGG dataset [17,18], the Free Firearm Sound Effects Library [19], the Gunshot audio dataset [20,21], the Gunshot Audio Forensics Dataset [22], the Gunshot/Gunfire Audio Dataset [23,24], and gunshot sounds from the Urbansound8k Dataset [25,26]. The 'other' sounds were also collected from online sources such as the Urbansound8k Dataset [25,26], the ESC-50 Dataset [27,28], the FSD50K dataset [29,30], and the snoring dataset [31,32].…”

COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans

“… [22] implemented COVID-19 respiratory sound classification task using 10 different classes of respiratory sounds. They proposed to use LSEDP (Local Symmetric Euclidean Distance Pattern) which used Euclidean distance to generate features.…”