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Fast recognition of bird sounds using extreme learning machines
Author(s) -
Qian Kun,
Guo Jian,
Ishida Ken,
Matsuoka Satoshi
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22378
Subject(s) - extreme learning machine , autoencoder , artificial neural network , support vector machine , artificial intelligence , training (meteorology) , computer science , machine learning , recall , pattern recognition (psychology) , geography , meteorology , psychology , cognitive psychology
Recognition of bird species by their sounds can bring considerable significance to both ecologists and ornithologists for measuring the biodiversity in the reserves, and studying climate changes. In this letter, we propose an efficient method based on an extreme learning machine (ELM) to classify bird sounds of 86 species of birds in very limited training and testing time. Experimental results prove that, the proposed ELM method can achieve the best recognition performance (81.1 %, unweighted average recall) compared with K ‐nearest neighbours ( K ‐NN), support vector machines (SVM), neural networks (NN), and deep neural networks (DNN) pre‐trained by an autoencoder. In addition, ELM requires the least total time for training and testing (2.047 ± 0.034 s). © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.