Wind Sounds Classification Using Different Audio Feature Extraction Techniques

Wala'A Nsaif Jasim,Esra'A Jasem Harfash,Saba Abdual Wahid Saddam

doi:10.31449/inf.v45i7.3739

Wala'A Nsaif Jasim, Esra'A Jasem Harfash + Show 1 more

Open Access

https://doi.org/10.31449/inf.v45i7.3739

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Journal: Informatica	Publication Date: Jan 10, 2022
Citations: 3	License type: cc-by

Affiliation: Iraq University College

Abstract

In this research, Different audio feature extraction technique are implemented and classification approaches are presented to classify seven types of wind. Where we applied features technique such as Zero Crossing Rate (ZCR) ,Fast Fourier Transformation (FFT), Linear predictive coding (LPC), Perceptual Linear Prediction (PLP). We know that some of these methods are good with human voices, but we tried to apply them here to characterize the wind audio content. The CNN classification method is implemented to determine the class of input wind sound signal. Experimental results show that each of these extraction feature methods are gave different results, but classification accuracy that are obtained of PLP features proven to have the best results.

Highlights

Processing of an audio signal generally includes extracting the most important features from it, analyzing, determining the presence of a specific pattern in the signal, and evaluating its behavior pattern, as well as how a particular signal is related to other similar signals
Performance of Zero Crossing Rate (ZCR), Fourier Transformation (FFT) and Linear predictive coding (LPC) is decreased with the increase of the number of classes
A Convolutional Neural Network (CNN)-Model of audio classification is used, because it is considered as a good performer in classification problems, so we adopted it in this work to measure its efficiency with this type of data

Summary

Introduction

Processing of an audio signal generally includes extracting the most important features from it, analyzing, determining the presence of a specific pattern in the signal, and evaluating its behavior pattern, as well as how a particular signal is related to other similar signals. The processing of the audio signal has clearly developed during the past few years, especially with regard to analyzing the audio signals and extracting the most important characteristics from and classifying it [1]. Any signal that represents a sound has a number of parameters such as amplitude, frequency, bandwidth, etc. These qualities can be used in many audio signal processors. Audio processing techniques involve the extraction of the features of a wave signal file, followed by decisionmaking schemes to detect and classify the inputted sound

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