Classifying Frog Calls Using Gaussian Mixture Models
We focus on the automatic classification of frog calls using shape features of spectrogram images. Monitoring frog populations is a means for tracking the health of natural habitats. This monitoring task is usually done by well-trained experts who listen and classify frog calls, which are tasks that are both time consuming and error prone. To automate this classification process, our method treats the sound signal of a frog call as a texture image, which is modeled as Gaussian mixture model. The method is simple but it has shown promising results. Tests performed on a dataset of frog calls of 15 different species produced an average classification rate of 80 %, which approximates human performance.
KeywordsFeature Extraction Gaussian Mixture Model Audio Signal Frog Species Cane Toad
The authors acknowledge support from National Science Foundation (NSF) grants No. 1263011 and No. 1152306. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
- 1.Hsu, A., Emerson, J., Levy, M., de Sherbinin, A., Johnson, L., Malik, O., Schwartz, J., Jaiteh, M.: The 2014 environmental performance index. Technical report,Yale Center for Environmental Law and Policy, New Haven, CT (2014)Google Scholar
- 2.Agency, U.E.P.: America’s wetlands : our vital link between land and water. Technical report, Office of Water, Office of Wetlands, Oceans, and Watersheds, Washington, DC (1995)Google Scholar
- 3.Knutson, M., Sauer, J., Olsen, D., Mossman, M., Hemesath, L., Lannoo, M.: Landscape associations of frog and toad species in iowa and wisconsin, USA. (2000)Google Scholar
- 9.Mossman, M.J., Hartman, L.M., Hay, R., Sauer, J.R., Dhuey, B.J.: Monitoring long-term trends in wisconsin frog and toad populations. In: Status and Conservation of Midwestern Amphibians, pp. 169–198 (1998)Google Scholar
- 10.Grigg, G., Taylor, A., Mc Callum, H., Watson, G.: Monitoring frog communities: an application of machine learning. In: Proceedings of Eighth Innovative Applications of Artificial Intelligence Conference, Portland Oregon, pp. 1564–1569 (1996)Google Scholar
- 13.Graciarena, M., Delplanche, M., Shriberg, E., Stolcke, A., Ferrer, L.: Acoustic front-end optimization for bird species recognition. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 293–296 (2010)Google Scholar
- 14.Belyaeva, N.A., Yash-yee, K.L., Smartc, K.M., Ribeirod, E.: Whatfrog: A comparison of classification algorithms for automated anuran recognitionGoogle Scholar
- 16.Xie, J., Towsey, M., Yasumiba, K., Zhang, J., Roe, P.: 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), pp. 1–6. IEEE (2015)Google Scholar
- 17.Visual Geometry Group: Department of Engineering Science, U.o.O. Texture classification (2007)Google Scholar