Fast recognition of bird sounds using extreme learning machines

详细信息    查看全文

• 作者：Kun Qian ; Jian Guo ; Ken Ishida and Satoshi Matsuoka
• 刊名：IEEJ Transactions on Electrical and Electronic Engineering
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：12
• 期：2
• 页码：294-296
• 全文大小：450K
• ISSN：1931-4981

文摘

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).