Frog classification using machine learning techniques

Authors:
Chenn-Jung Huang;Yi-Ju Yang;Dian-Xiu Yang;You-Jia Chen
Affiliations:
Department of Computer and Information Science, National Hualien University of Education, Taiwan;Institute of Ecology and Environmental Education, National Hualien University of Education, Taiwan;Department of Computer and Information Science, National Hualien University of Education, Taiwan;Department of Computer and Information Science, National Hualien University of Education, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 6
Cited 4

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Data Mining Applied to Acoustic Bird Species Recognition

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Bird species recognition using support vector machines

EURASIP Journal on Applied Signal Processing
Parametric Representations of Bird Sounds for Automatic Species Recognition

IEEE Transactions on Audio, Speech, and Language Processing
Content-based audio classification and retrieval by support vector machines

IEEE Transactions on Neural Networks

Intelligent preference selection model based on NRE for evaluating student learning achievement

Computers & Education
A call-independent and automatic acoustic system for the individual recognition of animals: A novel model using four passerines

Pattern Recognition
Automatic recognition of frog calls using a multi-stage average spectrum

Computers & Mathematics with Applications
Real-time classification via sparse representation in acoustic sensor networks

Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

An automatic frog sound identification system is developed in this work to provide the public to easily consult online. The sound samples are first properly segmented into syllables. Then three features, spectral centroid, signal bandwidth and threshold-crossing rate, are extracted to serve as the parameters for the frog sound classification. Two well-known classifiers, kNN and SVM, are adopted to recognize the frog species based on the three extracted features. The experimental results show that the average classification accuracy rate can be up to 89.05% and 90.30% for kNN and SVM classifiers, respectively. The effectiveness of the proposed on-line recognition system is thus verified.