Animal sound activity detection using multi-class support vector machines

• 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/3010/1/ICOM11-Animal_Sound_activity.pdf; http://irep.iium.edu.my/3010/; 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

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.