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Texture classification using spectral entropy of acoustic signal generated by a human echolocator

Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a...

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Main Authors:	Raja Abdullah, Raja Syamsul Azmir, Saleh, Nur Luqman, Syed Abdul Rahman, Sharifah Mumtazah, Zamri, Nur Syazmira, Abdul Rashid, Nur Emileen
Format:	Article
Language:	English
Published:	Multidisciplinary Digital Publishing Institute (MDPI) 2019
Online Access:	http://psasir.upm.edu.my/id/eprint/82160/1/Texture%20classification%20.pdf http://psasir.upm.edu.my/id/eprint/82160/ https://www.mdpi.com/1099-4300/21/10/963
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id	my.upm.eprints.82160
record_format	eprints
spelling	my.upm.eprints.821602020-12-16T14:56:30Z http://psasir.upm.edu.my/id/eprint/82160/ Texture classification using spectral entropy of acoustic signal generated by a human echolocator Raja Abdullah, Raja Syamsul Azmir Saleh, Nur Luqman Syed Abdul Rahman, Sharifah Mumtazah Zamri, Nur Syazmira Abdul Rashid, Nur Emileen Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation. Multidisciplinary Digital Publishing Institute (MDPI) 2019-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82160/1/Texture%20classification%20.pdf Raja Abdullah, Raja Syamsul Azmir and Saleh, Nur Luqman and Syed Abdul Rahman, Sharifah Mumtazah and Zamri, Nur Syazmira and Abdul Rashid, Nur Emileen (2019) Texture classification using spectral entropy of acoustic signal generated by a human echolocator. Entropy, 21 (10). art. no. 63. pp. 1-20. ISSN 1099-4300 https://www.mdpi.com/1099-4300/21/10/963 10.3390/e21100963
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
language	English
description	Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation.
format	Article
author	Raja Abdullah, Raja Syamsul Azmir Saleh, Nur Luqman Syed Abdul Rahman, Sharifah Mumtazah Zamri, Nur Syazmira Abdul Rashid, Nur Emileen
spellingShingle	Raja Abdullah, Raja Syamsul Azmir Saleh, Nur Luqman Syed Abdul Rahman, Sharifah Mumtazah Zamri, Nur Syazmira Abdul Rashid, Nur Emileen Texture classification using spectral entropy of acoustic signal generated by a human echolocator
author_facet	Raja Abdullah, Raja Syamsul Azmir Saleh, Nur Luqman Syed Abdul Rahman, Sharifah Mumtazah Zamri, Nur Syazmira Abdul Rashid, Nur Emileen
author_sort	Raja Abdullah, Raja Syamsul Azmir
title	Texture classification using spectral entropy of acoustic signal generated by a human echolocator
title_short	Texture classification using spectral entropy of acoustic signal generated by a human echolocator
title_full	Texture classification using spectral entropy of acoustic signal generated by a human echolocator
title_fullStr	Texture classification using spectral entropy of acoustic signal generated by a human echolocator
title_full_unstemmed	Texture classification using spectral entropy of acoustic signal generated by a human echolocator
title_sort	texture classification using spectral entropy of acoustic signal generated by a human echolocator
publisher	Multidisciplinary Digital Publishing Institute (MDPI)
publishDate	2019
url	http://psasir.upm.edu.my/id/eprint/82160/1/Texture%20classification%20.pdf http://psasir.upm.edu.my/id/eprint/82160/ https://www.mdpi.com/1099-4300/21/10/963
_version_	1687395155835879424
score	13.211869

Texture classification using spectral entropy of acoustic signal generated by a human echolocator

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