Frog classification using machine learning techniques

Chenn-Jung Huang a*, Yi-Ju Yang b, Dian-Xiu Yang a, You-Jia Chen a

aDepartment of Computer and Information SciencebInstitute of Ecology and Environmental Education

Expert Systems with Applications 36 (2009) 3737–3743, ELSEVIER

Presenter Chia-Cheng Chen 1

Introduction

Architecture of on-line frog sound identification system

Experimental results

Conclusion and feature work

Outline

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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.

Introduction

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Architecture of on-line frog sound identification system

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Architecture of on-line frog sound identification system(Cont.)

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Architecture of on-line frog sound identification system(Cont.)

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Signal preprocessing

◦Resampled at 8 kHz frequency and saved as 8-bit mono format

◦Normalized to the same level

Architecture of on-line frog sound identification system(Cont.)

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Syllable segmentation

1. Amplitude matrix S(a, t), initially n=1

2. Find an and tn, such that S(an, tn)=max{S(|a|, t)}

3. If |an| <= athreshold, stop the segmentation process. The athreshold

is the empirical threshold.

4. Store the amplitude trajectories corresponding to the nth

syllable in function An(τ), where τ=tn-ɛ,…,tn,…, tn+ɛ and is

the empirical threshold of the syllable.

Feature extraction

◦ Spectral centroid

◦ Signal bandwidth

Architecture of on-line frog sound identification system(Cont.)

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Architecture of on-line frog sound identification system(Cont.)

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◦ Threshold-crossing rate

Architecture of on-line frog sound identification system(Cont.)

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Classification

◦ kth nearest neighboring (KNN)

◦ Support vector machines (SVM)

Architecture of on-line frog sound identification system(Cont.)

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kth 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.

Architecture of on-line frog sound identification system(Cont.)

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Support vector machines (SVM)

Lagrangian Multiplier Method:

Architecture of on-line frog sound identification system(Cont.)

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Architecture of on-line frog sound identification system(Cont.)

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Experimental results

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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.

Conclusion and feature work

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