Evaluation of acoustic modality features for moving vehicle identification

In this article, the authors have studied various robust features for acoustic modality based moving vehicle identification system. Due to non-stationary signal characteristics of the vehicle and its aerodynamics, the features become prominent parameters for vehicle identification in the outdoor env...

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Veröffentlicht in:Multidimensional systems and signal processing 2022-12, Vol.33 (4), p.1349-1365
Hauptverfasser: Mohine, Shailesh, Gupta, Pooja, Bansod, Babankumar S., Bhalla, Rakesh, Basra, Anshul
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container_issue 4
container_start_page 1349
container_title Multidimensional systems and signal processing
container_volume 33
creator Mohine, Shailesh
Gupta, Pooja
Bansod, Babankumar S.
Bhalla, Rakesh
Basra, Anshul
description In this article, the authors have studied various robust features for acoustic modality based moving vehicle identification system. Due to non-stationary signal characteristics of the vehicle and its aerodynamics, the features become prominent parameters for vehicle identification in the outdoor environment. The proposed features are extracted from the experimentally generated dataset of moving vehicles. For computation of features, various feature extraction technique especially time domain, fast Fourier transform (FFT), short-time Fourier transform (STFT) and wavelets transform (WT) are applied on the vehicle’s signal. The essential features from above mentioned techniques are determined by recursive feature elimination (RFE) method. Furthermore, these proposed features are examined by Pearson correlation coefficient as well as boxplot parameters. It is observed that the Pearson correlation coefficient values for FFT based features i.e., spectral flux, MFCC-5, MFCC-7 and MFCC-13 are 0.13, 0.018, − 0.0073 and − 0.006, respectively to discriminate among the feature set of bus and gypsy. The boxplot parameters of spectral flux and MFCCs shows that there is significant variation in the experimental datasets of bus, gypsy and background noise. The SVM classifier has achieved the higher identification accuracy, 90% and better performance measures on FFT based features as compared to other feature extraction techniques for vehicle identification in the outdoor environment.
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subjects Artificial Intelligence
Background noise
Circuits and Systems
Correlation coefficients
Datasets
Electrical Engineering
Engineering
Fast Fourier transformations
Feature extraction
Fourier transforms
Parameter identification
Signal,Image and Speech Processing
Support vector machines
Time domain analysis
Vehicle identification
title Evaluation of acoustic modality features for moving vehicle identification
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