Comparison of neural network topologies for the classification of frogs by their songs
- 303 Downloads
At present the capture and recognition of sounds from different animal species have great value in making biological and environmental research in certain areas affected by humans. In this paper, two topologies for five species of anurans are proposed using a database that contains samples of sounds emitted by them. For the development of systems is performed processing audio signals to digitize it; then, the samples to extract the main features that will be our input system for subsequent classification methods using back-propagation and self-organized maps are processed.
KeywordsNeural network Perceptron MLP Self-organizing map MFCC Fourier
This study was no funding.
Compliance with ethical standards
Conflict of interest
There is no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Agostini M (2012) Myths and truths about toads and frogs. In: Frogs and toads background your home. Publishing house Universidad de La Plata (Edulp): Buenos Aires, Argentina, 37Google Scholar
- AmphibiaWebEcuador. Introduction. Ecuador, Museum of Zoology. http://zoologia.puce.edu.ec/Vertebrados/Anfibios/AnfibiosEcuador/Default.aspx
- Bardeli R, Wolff D, Kurth F, Koch M, Tauchert KH, Frommolt K-H (2010) Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recogn Lett 31(12): 1524–1534, 174, 175, 182Google Scholar
- Castaneda-Delgado JE, Cervantes-Villagrana AR, Rivas-Santiago B (2012) Antimicrobial peptides: a likely arsenal against HIV infection. Invest clín 53(1):71–83Google Scholar
- Chou C-H, Hsinchu Chung Hua Univ, Lee C-H, Ni H-W (2007) Bird species recognition by comparing the HMMs of the syllables. 5–7 Sept 2007Google Scholar
- Chou C-H, Liu P-H, Cai B (2008) On the studies of syllable segmentation and improving MFCCs for automatic birdsong recognition. In: Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference. Washington, DC, USA: IEEE Computer SocietyGoogle Scholar
- DARPA Neural Networks studies (1988) AFCEAInternational Press, p 60Google Scholar
- Data Mining Tools (2007) WEKA (Waikato Environment for Knowledge Analysis). Juan A. Botía Blaya. November 27, 2007Google Scholar
- Díaz Luis M, Cádiz Antonio (2007) Descriptive guide for identifying calls announcement of cuban eleutherodactylus frogs (anura: leptodactylidae)Google Scholar
- Inventory and Monitoring Techniques for amphibians of Tropical Andean region [online] pp 92, 2006. http://www.amphibians.org/wp-content/uploads/2013/07/Monitoreo-de-anfibios-baja-final.pdf
- Kohonen T (2001) Self-organizing maps. Springer: ISBN: 978-3540679219Google Scholar
- Lee C-H, Han C-C, Chuang C-C (2008) Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients. IEEE Trans Audio Speech Lang Process 16(8):1541–1550, 173, 175, 177, 181, 182Google Scholar
- López RF, Fernández JM (eds) (2013) Artificial neural networks: theoretical fundamentals and practical applications. Series, methodology and data analysis in social sciences. Netbiblo, pp 83-85Google Scholar
- Mesa D, Bernal A (2005) Protocols for the preservation and management of biological collections. Sci Bull Mus Center Mus Nat Hist 10:117–148Google Scholar
- Multivariate analysis (Diploma in Statistics). Item 3: Principal component analysis. http://halweb.uc3m.es/esp/Personal/personas/jmmarin/esp/AMult/AMult.html
- Nicolás T, Giraldo TN Salazar JT (2006) Anuran species recognition by their songs, audio files, using techniques of digital signal processing. Universidad Nacional branch office Manizales, December 2006Google Scholar
- Riedmiller (1993) Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, pp 586–591Google Scholar
- Selouani SA et al (2005) Automatic birdsong recognition based on autoregressive time-delay neural networks. In: Proceedings of ICSC Congress Computational Intelligence Methods and Applications, pp 1–6Google Scholar
- Semwal VB, Mondal K, Nandi GC (2015) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Appl 1–10. doi: 10.1007/s00521-015-2089-3
- Veintimilla D, Salinas K, Aguirre N (2014) Anuros diversity patterns in the paramo ecosystem of the Podocarpus National Park. Cedamaz, vol 2, septiembre, pp 31–39Google Scholar
- William R, González AMA (2014) Neural networks through shared maps in mobile devices. Int J Interact Multimed Artif Intel 3(1):28–35Google Scholar