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Dataset . 2022
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BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification

Authors: Cramer, Aurora; Lostanlen, Vincent; Evans, Bill; Farnsworth, Andrew; Salamon, Justin; Bello, Juan Pablo;

BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification

Abstract

BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification =============================================================== Version 2.0, February 2022. https://wp.nyu.edu/birdvox Description --------------- BirdVox-ANAFCC is a dataset of short audio waveforms, each of them containing a flight call from one of 14 birds of North America: four American sparrows, one cardinal, two thrushes, and seven New World warblers. * American Tree Sparrow (ATSP) * Chipping Sparrow (CHSP) * Savannah Sparrow (SAVS) * White-throated Sparrow (WTSP) * Red-breasted Grosbeak (RBGR) * Gray-cheeked Thrush (GCTH) * Swainson's Thrush (SWTH) * American Redstart (AMRE) * Bay-breasted Warbler (BBWA) * Black-throated Blue Warbler (BTBW) * Canada Warbler (CAWA) * Common Yellowthroat (COYE) * Mourning Warbler (MOWA) * Ovenbird (OVEN) It also contains other sounds which are often confused for one of the species above. These "confounding factors" encompass flight calls from other species of birds, vocalizations from non-avian animals, as well as some machine beeps. BirdVox-ANAFCC results from an aggregation of various smaller datasets, integrated under a common taxonomy. For more details on this taxonomy, we refer the reader to [1]: [1] Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. The second version of the BirdVox-ANAFCC dataset (v2.0) contains flight calls from the BirdVox-full-night dataset. These flight calls were present in the ICASSP 2020 benchmark but did not appear in the initial release of BirdVox-ANAFCC. Data Files ------------ BirdVox-ANAFCC contains the recordings as HDF5 files, sampled at 22,050 Hz, with a single channel (mono). Each HDF5 file contains flight call vocalizations of a particular species. The name of each HDF5 file follows the format: `<data-source>_<taxonomy-code>_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording varying in format depending on the data source. Metadata Files --------------- `taxonomy.yaml` details the three-level taxonomy structure used in this dataset, reflected in three-number-codes which largely follow "<family>.<order>.<species>". Additionally, at any level of the taxonomy, the numeric code "0" is reserved for "other" and the code "X" refers to unknown. For example, 1.1.0 corresponds to an American Sparrow with a species outside of our scope of interest, and 1.1.X corresponds to an American Sparrow of unknown species. At the top level (family), the "other" codes (0.\*.\*) deviate from the family-order-species in order to capture a variety of other out-of-scope sounds, including anthropophony, non-avian biophony, and biophony of avians outside of the scope of interest. Please acknowledge BirdVox-ANAFCC in academic research -------------------------------------------------------------------------- When BirdVox-ANAFCC is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication: Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors. Conditions of Use ---------------------- Dataset created by Aurora Cramer, Vincent Lostanlen, Bill Evans, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello. The BirdVox-ANAFCC dataset is offered free of charge under the terms of the Creative Commons Attribution International License: https://creativecommons.org/licenses/by/4.0/ The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-ANAFCC dataset or any part of it. Feedback ------------- Please help us improve BirdVox-full-night by sending your feedback to: vincent.lostanlen@gmail.com and auroracramer@nyu.edu In case of a problem, please include as many details as possible. Versions ------------ 1.0, May 2020: initial version, paired with ICASSP 2020 publication. 2.0, February 2022: added a missing dataset file (BirdVox-70k), updated name of first author (Aurora Cramer). Acknowledgement -------------------------- Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes. We thank contributors and maintainers of the Macaulay Library and the Xeno-Canto website. We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.

Related Organizations
Keywords

bioacoustics, machine learning, digital signal processing, bird vocalizations, flight calls, machine listening, ecology, acoustic signal detection, conservation science

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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