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...
| Main Authors: | , , , , |
|---|---|
| 格式: | 文件 |
| 语言: | English |
| 出版: |
Multidisciplinary Digital Publishing Institute (MDPI)
2019
|
| 在线阅读: | http://psasir.upm.edu.my/id/eprint/82160/1/Texture%20classification%20.pdf |
| _version_ | 1825951536407117824 |
|---|---|
| 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 | UPM |
| 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. |
| first_indexed | 2024-03-06T10:31:28Z |
| format | Article |
| id | upm.eprints-82160 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:31:28Z |
| publishDate | 2019 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | 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 |
| 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 |
| 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 |
| url | http://psasir.upm.edu.my/id/eprint/82160/1/Texture%20classification%20.pdf |
| work_keys_str_mv | AT rajaabdullahrajasyamsulazmir textureclassificationusingspectralentropyofacousticsignalgeneratedbyahumanecholocator AT salehnurluqman textureclassificationusingspectralentropyofacousticsignalgeneratedbyahumanecholocator AT syedabdulrahmansharifahmumtazah textureclassificationusingspectralentropyofacousticsignalgeneratedbyahumanecholocator AT zamrinursyazmira textureclassificationusingspectralentropyofacousticsignalgeneratedbyahumanecholocator AT abdulrashidnuremileen textureclassificationusingspectralentropyofacousticsignalgeneratedbyahumanecholocator |