1. Chenn-Jung Huang a*, Yi-Ju Yang b, Dian-Xiu Yang a, You-Jia Chen a a Department of Computer and Information Science b Institute of Ecology and Environmental Education Expert Systems with Applications 36 (2009) 3737–3743, ELSEVIER Presenter Chia-Cheng Chen 1

2.  Introduction  Architecture of on-line frog sound identification system  Experimental results  Conclusion and feature work 2

3.  An automatic frog sound identification system is developed in this work.  Three features, spectral centroid, signal bandwidth and threshold-crossing rate, are extracted to serve as the parameters for the frog sound classification. 3

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6. 6  Signal preprocessing ◦ Resampled at 8 kHz frequency and saved as 8-bit mono format ◦ Normalized to the same level

7. 7  Syllable segmentation 1.Amplitude matrix S(a, t), initially n=1 2.Find a n and t n, such that S(a n, t n )=max{S(|a|, t)} 3.If |a n | <= a threshold, stop the segmentation process. The a threshold is the empirical threshold. 4.Store the amplitude trajectories corresponding to the nth syllable in function A n (τ), where τ=t n - ɛ,…,t n,…, t n + ɛ and is the empirical threshold of the syllable.

8.  Feature extraction ◦ Spectral centroid ◦ Signal bandwidth 8

9. 9 ◦ Threshold-crossing rate

10. 10  Classification ◦ k th nearest neighboring (KNN) ◦ Support vector machines (SVM)

11. 11  k th nearest neighboring (KNN)  The kNN method is a simple yet effective method for classification in the areas of pattern recognition, machine learning, data mining, and information retrieval.

12. 12  Support vector machines (SVM) Lagrangian Multiplier Method:

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16.  An automatic frog sound identification system is proposed in this work to provide the public to consult online.  The sound samples are first properly segmented into syllables. 16