Time difference of arrival estimation of sound source using Cross Correlation and modified maximum likelihood weighting function

Hosseini, Mir Saber; Rezaie, Amir Hossein; Zanjireh, Yousef

doi:10.24200/sci.2017.4355

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…For this purpose, the Generalized Cross-Correlation Function (GCC) is most often used [ 56 ]. Localizing a moving sound source using the TDoA method is a problem due to the Doppler effect [ 57 ]. Angle of Arrival—This method determines the angle at which the sound wave reaches the microphone.…”

Section: Acoustic Source Detection and Localization Methodsmentioning

confidence: 99%

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A Survey of Sound Source Localization and Detection Methods and Their Applications

Jekateryńczuk,

Piotrowski

2023

Sensors

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This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

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Section: Acoustic Source Detection and Localization Methodsmentioning

confidence: 99%

Section: Acoustic Source Detection and Localization Methodsmentioning

confidence: 99%

A Survey of Sound Source Localization and Detection Methods and Their Applications

Jekateryńczuk,

Piotrowski

2023

Sensors

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“…s 1 (t) and s 2 (t) are the collected signals from sensor node 1 and sensor node 2, respectively. The generalized cross-correlation with weighting function [29] is as follows:…”

Section: Deployment Of Water Leakage Detection Systemmentioning

confidence: 99%

AIN-Based MEMS (Micro-Electro-Mechanical System) Hydrophone Sensors for IoT Water Leakage Detection System

Phua¹,

Rabeek²,

Han³

et al. 2020

Water

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There is an urgent need for industrial Internet of things (IoT) solutions to deploy a smart hydrophone sensor grid to monitor pipeline health and to provide an accurate prediction in the event of any leakage. One solution is to develop an IoT water leakage detection system consisting of an interface to capture acoustic signals from aluminum nitride (AlN)-based micro-machined infrasonic hydrophone sensors that are fed as inputs and predict an approximate leak location as a form of output. Micro-electro-mechanical systems (MEMS) are particularly useful for IoT applications with low power consumption and small device footprint. Data analytics including characterization, pre/post processing are applied to determine the leaks. In this work, we have developed the process flow and algorithm to detect pipe leakage occurrence and pinpoint the location accurately. Our approach can be implemented to detect leaks for different pipe lengths, diameters and materials.

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“…Zhu et al, suggested an increased accuracy of the DOA using Gaussian filters [33]. Hosseini et al, proposed a DOA determination based on the weighting functions which can successfully decrease the error range in TDOA [34].…”

Section: Introductionmentioning

confidence: 99%

A laboratory experiment for real-time sound source localization using Microsoft Kinect for Xbox 360

Douaer

2022

Preprint

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Sound Source Localization (SSL) consists of estimating the position of an emitting sound source. Accurate SSL is an essential step in many research areas as well as practical applications in acoustic signal processing and wireless communications. The main contribution of this work is the simplification of the high performance of two-dimensional sound source localization (2D-SSL), in which only four microphones are required. Firstly, the Time Difference Of Arrival (TDOA) based method is applied to a linear array configuration of four acquisition USB microphones, which have been used in two pairs to mount the experimental setup. For each single pair, the Angle of Arrival (AOA) of the sound source is calculated. Then, the 2D-SSL is determined using the trigonometric identities. To implement such a schematic, we have used the microphone array incorporated into the Microsoft Kinect device to acquire sound waves in real-time. To validate the accuracy of our study, laboratory experiments of sound source localization at different sound intensity levels were performed. In terms of RMSE position, the experimental results indicate that the suggested solution can be used to accurately find a two-dimensional sound source location in the far-field.

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Time difference of arrival estimation of sound source using Cross Correlation and modified maximum likelihood weighting function

Cited by 6 publications

References 14 publications

A Survey of Sound Source Localization and Detection Methods and Their Applications

A Survey of Sound Source Localization and Detection Methods and Their Applications

AIN-Based MEMS (Micro-Electro-Mechanical System) Hydrophone Sensors for IoT Water Leakage Detection System

A laboratory experiment for real-time sound source localization using Microsoft Kinect for Xbox 360

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