Animal sound activity detection using multi-class support vector machines

On March 11th 2011, the whole world was taken aback by another tragic experience of Tsunami triggered by a magnitude 9.8 earthquake in Japan. Just few days after that, on March 25th 2011, another earthquake of magnitude 6.8 hit Myanmar deaths and destructions. Despite the loss incurred on properties...

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Main Authors:	Astuti, Winda, Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka, Akmeliawati, Rini, Abdul Muthalif, Asan Gani
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://irep.iium.edu.my/3010/ http://irep.iium.edu.my/3010/ http://irep.iium.edu.my/3010/1/ICOM11-Animal_Sound_activity.pdf

id	iium-3010
recordtype	eprints
spelling	iium-30102011-10-13T03:11:37Z http://irep.iium.edu.my/3010/ Animal sound activity detection using multi-class support vector machines Astuti, Winda Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Akmeliawati, Rini Abdul Muthalif, Asan Gani TA Engineering (General). Civil engineering (General) On March 11th 2011, the whole world was taken aback by another tragic experience of Tsunami triggered by a magnitude 9.8 earthquake in Japan. Just few days after that, on March 25th 2011, another earthquake of magnitude 6.8 hit Myanmar deaths and destructions. Despite the loss incurred on properties and human being, available data show that relatively few numbers of animals died during most natural disasters. Prior to the occurrence of these disasters, available reports shows that animals do migrate to higher level or leave the areas en masse ahead of the event. Other related account show that animal sometimes behaves in unusual ways prior to the occurrence of these natural disasters. These overwhelming evidences point to the fact that animals might have the ability to sense impending natural disaster precursor signals ahead of time. This paper discusses the preliminary results obtained from the use of support vector machine (SVM) and Mel-frequency cepstral coefficients (MFCC) in the development of animal sound activity detection (ASAD) which is an integral part in the development of earthquake and natural disaster prediction using unusual animal behavior. The use of MFCC has been proposed for the features extraction stage while SVM has been proposed for classification of the extracted features. Preliminary results obtained shows that the MFCC and SVM can be used for features extraction and features classification respectively. 2011-05-17 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/3010/1/ICOM11-Animal_Sound_activity.pdf Astuti, Winda and Aibinu, Abiodun Musa and Salami, Momoh Jimoh Emiyoka and Akmeliawati, Rini and Abdul Muthalif, Asan Gani (2011) Animal sound activity detection using multi-class support vector machines. In: 4th International Conference on Mechatronics (ICOM) , 17-19 May, 2011 , Kuala Lumpur. http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5937122&queryText%3DAnimal+Sound+Activity+Detection+using+multi-class+support+machines%26openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Astuti, Winda Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Akmeliawati, Rini Abdul Muthalif, Asan Gani Animal sound activity detection using multi-class support vector machines
description	On March 11th 2011, the whole world was taken aback by another tragic experience of Tsunami triggered by a magnitude 9.8 earthquake in Japan. Just few days after that, on March 25th 2011, another earthquake of magnitude 6.8 hit Myanmar deaths and destructions. Despite the loss incurred on properties and human being, available data show that relatively few numbers of animals died during most natural disasters. Prior to the occurrence of these disasters, available reports shows that animals do migrate to higher level or leave the areas en masse ahead of the event. Other related account show that animal sometimes behaves in unusual ways prior to the occurrence of these natural disasters. These overwhelming evidences point to the fact that animals might have the ability to sense impending natural disaster precursor signals ahead of time. This paper discusses the preliminary results obtained from the use of support vector machine (SVM) and Mel-frequency cepstral coefficients (MFCC) in the development of animal sound activity detection (ASAD) which is an integral part in the development of earthquake and natural disaster prediction using unusual animal behavior. The use of MFCC has been proposed for the features extraction stage while SVM has been proposed for classification of the extracted features. Preliminary results obtained shows that the MFCC and SVM can be used for features extraction and features classification respectively.
format	Conference or Workshop Item
author	Astuti, Winda Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Akmeliawati, Rini Abdul Muthalif, Asan Gani
author_facet	Astuti, Winda Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Akmeliawati, Rini Abdul Muthalif, Asan Gani
author_sort	Astuti, Winda
title	Animal sound activity detection using multi-class support vector machines
title_short	Animal sound activity detection using multi-class support vector machines
title_full	Animal sound activity detection using multi-class support vector machines
title_fullStr	Animal sound activity detection using multi-class support vector machines
title_full_unstemmed	Animal sound activity detection using multi-class support vector machines
title_sort	animal sound activity detection using multi-class support vector machines
publishDate	2011
url	http://irep.iium.edu.my/3010/ http://irep.iium.edu.my/3010/ http://irep.iium.edu.my/3010/1/ICOM11-Animal_Sound_activity.pdf
first_indexed	2023-09-18T20:10:42Z
last_indexed	2023-09-18T20:10:42Z
_version_	1777407458090680320

Animal sound activity detection using multi-class support vector machines

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