Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks

A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar m...
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Autor*in:	Saulius Sakavičius [verfasserIn] Artūras Serackis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	acoustic source localization multiple source localization machine learning tetrahedral sensor arrays

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 10(2021), 21, p 2585
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:21, p 2585

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics10212585

Katalog-ID:	DOAJ029930685

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language	English
source	In Electronics 10(2021), 21, p 2585 volume:10 year:2021 number:21, p 2585
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topic_facet	acoustic source localization multiple source localization machine learning tetrahedral sensor arrays Electronics
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callnumber-first	T - Technology
author	Saulius Sakavičius
spellingShingle	Saulius Sakavičius misc TK7800-8360 misc acoustic source localization misc multiple source localization misc machine learning misc tetrahedral sensor arrays misc Electronics Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks
authorStr	Saulius Sakavičius
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topic_title	TK7800-8360 Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks acoustic source localization multiple source localization machine learning tetrahedral sensor arrays
topic	misc TK7800-8360 misc acoustic source localization misc multiple source localization misc machine learning misc tetrahedral sensor arrays misc Electronics
topic_unstemmed	misc TK7800-8360 misc acoustic source localization misc multiple source localization misc machine learning misc tetrahedral sensor arrays misc Electronics
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title	Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks
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title_full	Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks
author_sort	Saulius Sakavičius
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title_sort	estimation of azimuth and elevation for multiple acoustic sources using tetrahedral microphone arrays and convolutional neural networks
callnumber	TK7800-8360
title_auth	Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks
abstract	A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.
abstractGer	A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.
abstract_unstemmed	A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.
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title_short	Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks
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