Abstract
Physiological research reported that certain frog species contain antimicrobial substances which is potentially and beneficial in overcoming certain health problem. As a result, there is an imperative need for an automated frog species identification to assist people in physiological research in detecting and localizing certain frog species. This project aims to develop a frog sound identification system which is expected to recognize frog species according to the recorded bio acoustic signals. The Mel Frequency Cepstrum Coefficient (MFCC) and Linear Predictive Coding (LPC) are used as the feature extraction techniques for the system while the classifier employed is k-Nearest Neighbor (K-NN). Database from AmphibiaWeb has been used to evaluate the system performances. Experimental results showed that system performances of 98.1% and 93.1% have been achieved for MFCC and LPC techniques, respectively.
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Ting Yuan, C.L., Athiar Ramli, D. (2013). Frog Sound Identification System for Frog Species Recognition. In: Vinh, P.C., Hung, N.M., Tung, N.T., Suzuki, J. (eds) Context-Aware Systems and Applications. ICCASA 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36642-0_5
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DOI: https://doi.org/10.1007/978-3-642-36642-0_5
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