frog classification using machine learning techniques
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Frog classification using machine learning techniques. 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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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.
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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
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Architecture of on-line frog sound identification system(Cont.)
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Classification
◦ kth nearest neighboring (KNN)
◦ Support vector machines (SVM)
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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.
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Architecture of on-line frog sound identification system(Cont.)
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Support vector machines (SVM)
Lagrangian Multiplier Method:
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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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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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