Western Mediterranean Wetland Birds dataset: A new annotated dataset for acoustic bird species classification |
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| Institution: | 1. Civil Engineering Department, University of Sistan and Baluchestan, Zahedan, Iran;2. Water Security & Sustainable Development Hub, School of Engineering, Newcastle University, Newcastle Upon Tyne, UK;3. School of Engineering, Newcastle University, Newcastle upon Tyne, UK;4. Chair of Engineering Hydrology and Water Management, Technical University of Darmstadt, Darmstadt, Germany;1. Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Purba Bardhaman, 713104, India;2. Department of Geography, The University of Burdwan, Purba Bardhaman, 713104, West Bengal, India;3. Department of Basic Sciences and Humanities, Institute of Engineering & Management, Sector -V, Salt Lake City, Kolkata 700091, West Bengal, India;1. Shijiazhuang Institute of Railway Technology, Shijiazhuang 050018, China;2. School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;3. College of Forestry, Beijing Forestry University, Beijing 100083, China;4. Pingwu Panda Valley Family Farm, Pingwu 622550, China;5. The Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;6. UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia;1. Laboratório de Parasitologia Médica e Biologia de Vetores, Faculdade de Medicina, Universidade de Brasília, Brasília, DF, Brazil;2. Programa de Pós-Graduação em Zoologia, Departamento de Zoologia, Universidade de Brasília, Brasília, DF, Brazil;3. Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523, USA;4. Biodiversity Institute, University of Kansas, Lawrence, KS, USA |
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| Abstract: | The deployment of an expert system running over a wireless acoustic sensors network made up of bioacoustic monitoring devices that recognize bird species from their sounds would enable the automation of many tasks of ecological value, including the analysis of bird population composition or the detection of endangered species in areas of environmental interest. Endowing these devices with accurate audio classification capabilities is possible thanks to the latest advances in artificial intelligence, among which deep learning techniques stand out. To train such algorithms, data from the sources to be classified is required. For this reason, this paper presents the Western Mediterranean Wetland Birds (WMWB) dataset, consisting of 201.6 min and 5795 annotated audio excerpts of 20 endemic bird species of the Aiguamolls de l'Empordà Natural Park. The main objective of this work is to describe and analyze this new dataset. Moreover, this work presents the results of bird species classification experiments using four well- known deep neural networks fine-tuned on our dataset, whose models are also made public along with the dataset. These results are aimed to serve as a performance baseline reference for the community when using the WMWB dataset for their experiments. |
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