Wavelet-based acoustic detection of moving vehicles

Averbuch, Amir; Zheludev, Valery A.; Rabin, Neta; Schclar, Alon

doi:10.1007/s11045-008-0058-z

Cited by 56 publications

(33 citation statements)

References 12 publications

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“…Another paper which makes use of the wavelet packet transform to extract features is [3]. The algorithm distinguishes between vehicles and background.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed algorithm extracts features by applying the 6th order spline wavelet packet, which is described in Appendix A and in more detail in [2,3]. Specifically, the wavelet coefficients are divided to frequency equi-width bands.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Build a Gaussian kernel using 0 such that k 0 (x, y) = exp (− x − y 2 /2 0 ). 3. Compute the set of eigenvectors for this kernel.…”

Section: B21 the Nyström Extensionmentioning

confidence: 99%

See 2 more Smart Citations

A diffusion framework for detection of moving vehicles

Schclar

Averbuch

Rabin

et al. 2010

Digital Signal Processing

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“…Another paper which makes use of the wavelet packet transform to extract features is [3]. The algorithm distinguishes between vehicles and background.…”

Section: Related Workmentioning

confidence: 99%

Section: The Proposed Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A diffusion framework for detection of moving vehicles

Schclar

Averbuch

Rabin

et al. 2010

Digital Signal Processing

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“…Wavelet method has been used for feature extraction. Similar features extraction method has been made by Amir Averbuch [3,4]. Huadong Wu et.…”

Section: Introductionmentioning

confidence: 99%

Classification of Moving Vehicles using Multi-Classifier with Time-Domain Approach

AbdulRahim¹,

Paulraj²,

Adom³

2013

IJCA

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The hearing impaired is afraid of walking along a street and living a life alone. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoors. The sound produced by moving vehicle in outdoor situation cannot be moderated wisely by profoundly hearing impaired community. They also cannot distinguish the type and the distance of any moving vehicle approaching from their behind. In this paper, a simple system that identifies the type and distance of a moving vehicle using artificial neural network has been proposed. The noise emanated from a moving vehicle along the roadside was recorded together with its type and position. Using time-domain approach, simple feature extraction algorithm for extracting the feature from the noise emanated by the moving vehicle has been developed. Simple time-domain features such as energy and zerocrossing rates are applied for getting the important signatures from the sound. The extracted features were associated with the type and zone of the moving vehicle and a multi-classifier system (MCS) based on neural network model has been developed. The developed MCS is tested for its validity. General TermsPattern Recognition, Moving Vehicle, Multilayer Perceptron

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“…The material of Chap. 12 is based on [11].Detection of incipient bearing fault in a slowly rotating machine using spline wavelet packets: Chap. 13 describes a successful application of spline-based wavelet packet transforms originating from periodic discrete splines to a complicated problem of detection of incipient defects in rolling element bearings by the analysis of recorded vibration signals.…”

mentioning

confidence: 99%

Spline and Spline Wavelet Methods with Applications to Signal and Image Processing

Averbuch

Neittaanmäki

Zheludev

2019

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Additional material to this book can be downloaded from http://extras.springer.com. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paperThis Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland To Dorit Peled-Averbuch Neittaanmäki Family Tatiana Zheludev PrefaceThis volume concludes our three-volume series titled Spline and spline wavelet methods with applications to signal and image processing.Volume I [12] systematically describes the design and applications of periodic polynomial and discrete splines and wavelets, wavelet packets and wavelet frames that originate from these splines. As a working tool in [12], the so-called Spline Harmonic Analysis (SHA) [29,30] is used, which combines approximation abilities of splines with the computational strength of the fast Fourier transform (FFT). It introduces the harmonic analysis methodologies into periodic spline spaces. SHA enables us to efficiently construct and manipulate different types of splines, wavelets, wavelet packets, and wavelet frames. SHA has paved the way for periodic splines to contribute to solutions for applied signal/image processing problems [6-9, 11, 30, 31]. A few applications are presented in [12], such as Upsampling of discrete-time signals/images: It is done using binary and ternary spline subdivision. Signals' deconvolution by regularized matching pursuit (RMP): The RMP method that uses orthonormal spline wavelet packet dictionaries, restores signals blurred by convolution with a bandlimited kernel and corrupted by strong noise. Block-based deconvolution and inversion of the heat equation: When a signal/image is restored from blurred sampled data affected by noise, relative contributions of a coherent signal and noise are di...

show abstract

Wavelet-based acoustic detection of moving vehicles

Cited by 56 publications

References 12 publications

A diffusion framework for detection of moving vehicles

A diffusion framework for detection of moving vehicles

Classification of Moving Vehicles using Multi-Classifier with Time-Domain Approach

Spline and Spline Wavelet Methods with Applications to Signal and Image Processing

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