Frog classification using machine learning techniques
This paper presents an innovative automatic frog sound identification system developed through advanced machine learning techniques. Key features extracted for sound classification include spectral centroid, signal bandwidth, and threshold-crossing rate. The architecture comprises signal preprocessing, syllable segmentation, and robust classification methods such as the k-nearest neighbors (kNN) and support vector machines (SVM). Experimental results demonstrate the effectiveness of the system, emphasizing its potential for ecological research and public accessibility of frog sound data.
Frog classification using machine learning techniques
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Presentation Transcript
Frog classification using machine learning techniques Chenn-Jung Huang a*, Yi-Ju Yang b, Dian-Xiu Yang a, You-Jia Chen a aDepartmentof Computer and Information Science bInstituteof Ecology and Environmental Education Expert Systems with Applications 36 (2009) 3737–3743, ELSEVIER Presenter Chia-Cheng Chen
Outline • Introduction • Architecture of on-line frog sound identification system • Experimental results • Conclusion and feature work
Introduction • 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 forthe frog sound classification.
Architecture of on-line frog sound identification system(Cont.)
Architecture of on-line frog sound identification system(Cont.) • 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.) • Syllable segmentation • Amplitude matrix S(a, t), initially n=1 • Find an and tn, such that S(an, tn)=max{S(|a|, t)} • If |an| <=athreshold, stop the segmentation process. The athreshold is the empirical threshold. • 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.
Architecture of on-line frog sound identification system(Cont.) • Feature extraction • Spectral centroid • Signal bandwidth
Architecture of on-line frog sound identification system(Cont.) • Threshold-crossing rate
Architecture of on-line frog sound identification system(Cont.) • Classification • kth nearest neighboring (KNN) • Support vector machines (SVM)
Architecture of on-line frog sound identification system(Cont.) • kth nearest neighboring (KNN) • The kNN method is a simple yet effective method for classification in the areas of pattern recognition, machine learning, datamining, and information retrieval.
Architecture of on-line frog sound identification system(Cont.) • Support vector machines (SVM) Lagrangian Multiplier Method:
Architecture of on-line frog sound identification system(Cont.)
Architecture of on-line frog sound identification system(Cont.)
Conclusion and feature work • An automatic frog sound identification system is proposed inthis work to provide the public to consult online. • The sound samples are first properly segmented into syllables.
