@article{39bfee2541344a5bb27084b0c796e208,
title = "Western Mediterranean Wetland Birds dataset: A new annotated dataset for acoustic bird species classification",
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'Empord{\`a} 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.",
keywords = "Audio dataset, Bird song, Deep learning, Neural network, Species identification, Spectrogram",
author = "Joan G{\'o}mez-G{\'o}mez and Ester Vida{\~n}a-Vila and Xavier Sevillano",
note = "Funding Information: Authors would like to thank the Departament de Recerca i Universitats (Generalitat de Catalunya) under Grant Ref. 2021 SGR 01396 . They would also like to acknowledge Albert Brugas Riera and Sergio Romero de Tejada Mart'ınez from the Aiguamolls de l'Empord{\`a} Natural Park for their valuable help when defining the species of interest to be classified. Also, authors would like to acknowledge all the Xeno-Canto community and their contributors for making the creation of the dataset possible. Specially, authors would like to thank the following contributors for giving us special permission to use their recordings in this work despite having uploaded them on the Xeno-Canto portal under the terms BY-NC-ND: Anh{\"a}user, Arnold Meijer, Bodo Sonnenburg, Chie-Jen Jerome Ko, Ding Li Yong, Eveny Luis, Fernand Deroussen (Sonoth{\`e}que du MNHN), Hans Matheve, Herman van der Meer, Itziar Guti{\'e}rrez, Jacques Prevost, Jarek Matusiak, J{\'e}r{\'e}my Simar, Joost van Bruggen, Krzysztof Deoniziak, Lars Lachmann, Mandar Bhagat, Marc Anderson, Marco Dragonetti ( www.birdsongs.it ), Matthias, Feuersenger, Maudoc, Niels Krabbe, Patrick Franke, Peter Boesman, Piotr Szczypinski, Ruud van Beusekom. Publisher Copyright: {\textcopyright} 2023",
year = "2023",
month = jul,
doi = "10.1016/j.ecoinf.2023.102014",
language = "English",
volume = "75",
journal = "Ecological Informatics",
issn = "1574-9541",
publisher = "Elsevier B.V.",
}