Fast recognition of bird sounds using extreme learning machines. Issue 2 (15th December 2016)

• Record Type: Journal Article
• Authors: Qian, Kun; Guo, Jian; Ishida, Ken; Matsuoka, Satoshi
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 12:Issue 2(2017)
• Page Start: 294
• Page End: 296
• Publication Date: 2016-12-15
• Subjects: bio‐acoustics -- ecology -- bird sounds -- openSMILE -- extreme learning machines; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22378
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : 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.