An automated multispecies bioacoustics sound classification method based on a nonlinear pattern: Twine-pat |
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| Affiliation: | 1. Bioacoustics Research Program, Cornell Lab of Ornithology, United States;2. Conservation Science Program, Cornell Lab of Ornithology, United States;3. Powdermill Avian Research Center, United States;4. Information Science, Cornell Lab of Ornithology, United States;1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China;2. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Jiangnan University, Wuxi 214122, PR China;3. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;4. Business School, Nanjing University, Nanjing, China;5. Department of Economics, University of Ottawa, Ontario, Canada;1. Institute for Technological Development and Innovation in Communications, Spain;2. Signal and Communications Department, Spain;3. Telematic Engineering Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira S/N, 35017 Las Palmas de Gran Canaria, Spain;2. National Institute of Limnology, INALI, UNL-CONICET, Argentina |
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| Abstract: | Categorizing the bioacoustic and ecoacoustic properties of animals is great interest to biologists and ecologists. Also, multidisciplinary studies in engineering have significantly contributed to the development of acoustic analysis. Observing the animals living in the ecological environment provides information in many areas such as global warming, climate changes, monitoring of endangered animals, agricultural activities. However, the classification of bioacoustics sounds by manually is very hard. Therefore, automated bioacoustics sound classification is crucial for ecological science. This work presents a new multispecies bioacoustics sound dataset and novel machine learning model to classify bird and anuran species with sounds automatically. In this model, a new nonlinear textural feature generation function is presented by using twine cipher substitution box(S-box), and this feature generation function is named twine-pat. By using twine-pat and tunable Q-factor wavelet transform, a multilevel feature generation network is presented. Iterative ReliefF(IRF) is employed to select the most effective/valuable features. Two shallow classifiers are used to calculate results. Our presented model reached 98.75% accuracy by using k-nearest neighbor(kNN) classifier. The results obviously demonstrated the success of the presented model. |
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