Detection of anuran calling activity in long field recordings for bio-acoustic monitoring
Data(s) |
2015
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Resumo |
This paper presents a system to analyze long field recordings with low signal-to-noise ratio (SNR) for bio-acoustic monitoring. A method based on spectral peak track, Shannon entropy, harmonic structure and oscillation structure is proposed to automatically detect anuran (frog) calling activity. Gaussian mixture model (GMM) is introduced for modelling those features. Four anuran species widespread in Queensland, Australia, are selected to evaluate the proposed system. A visualization method based on extracted indices is employed for detection of anuran calling activity which achieves high accuracy. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/89672/1/Detection%20of%20anuran%20calling%20activity%20in%20long%20field%20recordings%20for%20bio-acoustic%20monitoring%20-%20final.pdf DOI:10.1109/ISSNIP.2015.7106925 Xie, Jie, Towsey, Michael, Yasumiba, Kiyomi, Zhang, Jinglan, & Roe, Paul (2015) Detection of anuran calling activity in long field recordings for bio-acoustic monitoring. In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, Singapore, pp. 1-6. |
Direitos |
Copyright 2015 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Fonte |
Science & Engineering Faculty |
Palavras-Chave | #050202 Conservation and Biodiversity #080104 Computer Vision #080106 Image Processing #090609 Signal Processing #anzsrc Australian and New Zealand Standard Research Class #Anuran calling activity detection #canetoad detection #frog detection #spectral peak track #Gaussian mixture model |
Tipo |
Conference Paper |