Abstract   Automated identification of animals based their acoustic sound is now preferable by biologist in assisting them to identify animal species for environmental monitoring work. This approach is gradually replacing manual techniques that claimed to be costly and time-consuming. However, it is a challenging task to execute the automated system when the environment is in noisy condition especially in the presence of non-stationary noises such as insect sounds or multiple animal sounds from different species. In this paper, a combination of enhanced start and end point detection namely short time energy (STE) and short time average zero crossing rates (STAZCR) is proposed to improve the syllable segmentation. In this approach, a novel peak finding algorithm is integrated to iteratively narrow down the numbers of local minima and maxima in order to determine the true local maximum value. In this study, the bioacoustics sound samples from frog call database, consists of six hundred and seventy-five frog call data from 15 frog species, recorded in forests located in Kulim and Baling, Malaysia are used. The experimental results demonstrate that 94.13% of performance is achieved by using the proposed method i.e. combination of STE and STAZCR compared to 81.6% of performance for the baseline method, i.e. the combination of the energy and ZCR.
['Dzati Athiar Ramli', 'Haryati Jaafar']
Peak Finding Algorithm to Improve Syllable Segmentation for Noisy Bioacoustic Sound Signal