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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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Autores principales: Raja Abdullah, Raja Syamsul Azmir, Saleh, Nur Luqman, Syed Abdul Rahman, Sharifah Mumtazah, Zamri, Nur Syazmira, Abdul Rashid, Nur Emileen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514294/
http://dx.doi.org/10.3390/e21100963
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author Raja Abdullah, Raja Syamsul Azmir
Saleh, Nur Luqman
Syed Abdul Rahman, Sharifah Mumtazah
Zamri, Nur Syazmira
Abdul Rashid, Nur Emileen
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
collection PubMed
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.
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spelling pubmed-75142942020-11-09 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 Entropy (Basel) Article 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. MDPI 2019-10-02 /pmc/articles/PMC7514294/ http://dx.doi.org/10.3390/e21100963 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514294/
http://dx.doi.org/10.3390/e21100963
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