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17 results for “flight calls”
BirdVox-full-night: a dataset for avian flight call detection in continuous recordings
<p>BirdVox-full-night: a dataset for avian flight call detection in continuous recordings<br> ======================================================================================<br> Version 3.0, March 2018.</p> <p><br> Created By<br> ----------</p> <p>Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3).</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Center for Urban Science and Progress, New York University<br> (3): Music and Audio Research Lab, New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> -----------</p> <p>The BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10.</p> <p>Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.</p> <p>The dataset can be used, among other things, for the research,<br> development and testing of bioacoustic classification models, including the reproduction of the results reported in [1].</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[2] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-full-night_flac-audio folder contains the recordings as FLAC files, sampled at 24 kHz, with a single channel (mono).</p> <p> </p> <p>Metadata Files<br> --------------</p> <p>The BirdVox-full-night_csv-annotations folder contains JAMS files, where each row correspond to a different location in the time frequency domain (columns "Time (s)" and "Freq (Hz)").</p> <p>The approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) and UTC timestamps corresponding to the start of the recording for each sensor are included as CSV files in the main directory.</p> <p> </p> <p>Please acknowledge BirdVox-full-night in academic research<br> ----------------------------------------------------------</p> <p>When BirdVox-full-night is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.</p> <p>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.</p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>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, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -----------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> ----------------</p> <p>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.</p> <p>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.</p>
BirdVox-296h: a large-scale dataset for detection and classification of flight calls
<p>BirdVox 296 hours dataset (BirdVox-296h)<br> ====================================</p> <p>Version 2.1, May 2022.</p> <p><br> Created By<br> ----------</p> <p>Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4)</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS<br> (3): Adobe Research<br> (4): New York University</p> <p>https://wp.nyu.edu/birdvox<br> <br> </p> <p>Description<br> ---------------</p> <p>The BirdVox-296h dataset contains 148 audio recordings, each two hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured by nine different sensors, originally numbered 1, 2, 3, 4, 5, 6, 7, 8, and 10.<br> <br> Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours.</p> <p>The dataset can be used, among other things, for the research, development and testing of machine listening models for bird migration monitoring.</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-296h_wav folder contains 148 recordings as WAV files, sampled at 24 kHz, with a single channel (mono). Each recording lasts exactly two hours and is named according to the following format:</p> <p>YYYY-MM-DD_hh-mm-ss_unitUU.wav</p> <p>Where Y means Year, M means Month, D means Day, h means hour, m means minute, and s means second. This date format corresponds to the start time of the recording file, expressed in Coordinated Universal Time (UTC).</p> <p>The field UU contains two digits corresponding to the identifier of the autonomous recording unit (i.e., bioacoustic sensor). UU is either equal to 01, 02, 03, 04, 05, 06, 07, 08, or 10. Note that 09 is absent from the list because sensor 09 failed during the acquisition campaign.</p> <p> </p> <p>Metadata Files<br> -------------------</p> <p>The BirdVox-296h_csv-annotations folder contains CSV files, one for each audio file. The columns of each CSV file are:</p> <p>ID,Time (s),Frequency (Hz),Taxonomy Code,Fine Label,Medium Label,Coarse Label</p> <p><br> "Taxonomy Code" is compliant with the BirdVoxClassify software: github.com/BirdVox/BirdVoxClassify</p> <p>"Fine Label", "Medium Label", and "Coarse Label" most often correspond to species, family and order respectively.</p> <p> </p> <p>The BirdVox-296h_gps-coordinates.csv file contains the approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) of all nine sensors.</p> <p> </p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>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, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -------------</p> <p>Please help us improve BirdVox-296h by sending your feedback to:<br> vincent.lostanlen@ls2n.fr and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> --------------------------</p> <p>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.</p> <p>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.</p>
BirdVox-25SD: a dataset of flight calls with species annotations
<pre>BirdVox 25 Species Dataset (BirdVox-25SD) ============= Version 1.0, Jan 2021. Created By ---------- Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4) (1): Cornell Lab of Ornithology (CLO) (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS (3): Adobe Research (4): New York University https://wp.nyu.edu/birdvox Description ----------- The BirdVox 25 Species Dataset (BirdVox-25SD) contains 26,124 audio clips of avian flight calls, each ranging from about 150 ms to 500 ms in duration. The clips are extracted from the <a href="http://https://doi.org/10.5281/zenodo.4603643">BirdVox-296h</a> dataset using the corresponding annotations. The recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the 2015 migration season (August - November). The dataset can be used, among other things, for the research, development and testing of bioacoustic classification models. For details on the hardware of ROBIN recording units, we refer the reader to [1]. [1] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016. Changes from BirdVox 14-SD ---------------------------- This dataset builds upon the <a href="http://https://doi.org/10.5281/zenodo.3667094">BirdVox 14 Species Dataset (BirdVox-14SD)</a>, adding ~12,000 audio clips and annotations. The annotation taxonomy has been expanded to add a new order, a new family, and 11 new species. Additionally, the audio clips are more accurately aligned to the annotation times. For backwards compatibility with the BirdVox-14SD taxonomy, we include the file `birdvox25sd-to-birdvox14sd-taxonomy-code-map.csv` which maps BirdVox-25SD taxonomy codes to BirdVox-14SD taxonomy codes. Taxonomic Annotations ----------------------- Classification annotations for each flight call are given at three taxonomic levels: order, family, and species. These annotations are condensed into a three-number-code which largely follow "..". The specific numeric codes are: * Order * 1.\*.\* - Passeriformes * 2.\*.\* - Pelecaniformes * Family * 1.1.\* - American Sparrow * 1.2.\* - Cardinals * 1.3.\* - Thrushes * 1.4.\* - New World warblers * 2.1.\* - Herons * Species * 1.1.1 - American tree sparrow (ATSP) * 1.1.2 - Chipping sparrow (CHSP) * 1.1.3 - Savannah sparrow (SAVS) * 1.1.4 - White-throated sparrow (WTSP) * 1.1.5 - Song sparrow (SOSP) * 1.2.1 - Rose-breasted grosbeak (RBGR) * 1.3.1 - Gray-cheeked thrush (GCTH) * 1.3.2 - Swainson's thrush (SWTH) * 1.3.3 - Hermit thrush (HETH) * 1.3.4 - Veery (VEER) * 1.3.5 - Wood thrush (WOTH) * 1.4.1 - American redstart (AMRE) * 1.4.2 - Bay-breasted warbler (BBWA) * 1.4.3 - Black-throated blue warbler (BTBW) * 1.4.4 - Canada warbler (CAWA) * 1.4.5 - Common yellowthroat (COYE) * 1.4.6 - Mourning warbler (MOWA) * 1.4.7 - Ovenbird (OVEN) * 1.4.8 - Black-and-white warbler (BAWW) * 1.4.9 - Cape May warbler (CMWA) * 1.4.10 - Chestnut-sided warbler (CSWA) * 1.4.11 - Northern Parula (NOPA) * 1.4.12 - Wilson's warbler (WIWA) * 1.4.13 - Yellow-rumped warbler (YRWA) * 2.1.1 - Green heron (GRHE) 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 refer to `<a href="https://zenodo.org/record/5856260/files/BirdVox-296h_taxonomy.yaml">BirdVox-296h_taxonomy.yaml</a>` in <a href="http://https://doi.org/10.5281/zenodo.5856260">BirdVox-296h</a> for the details of this taxonomy structure. Data Files ------------ BirdVox-25SD 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: `BirdVox-25SD-v1pt0_{taxonomy_code}_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording following the format: `unit-{unit_num}`. Conditions of Use ---------------------- Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello. The BirdVox-25SD dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International License. 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, CLO is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-25SD dataset or any part of it. Feedback ----------- Please help us improve BirdVox-25SD 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. Acknowledgements ------------------------ 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 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. The creation of this dataset was supported by NSF grants 1633259 (BIRDVOX).</pre>
BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification
<p>BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification<br> ===============================================================<br> Version 2.0, February 2022.</p> <p>https://wp.nyu.edu/birdvox</p> <p><br> Description<br> ---------------</p> <p>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.<br> * American Tree Sparrow (ATSP)<br> * Chipping Sparrow (CHSP)<br> * Savannah Sparrow (SAVS)<br> * White-throated Sparrow (WTSP)<br> * Red-breasted Grosbeak (RBGR)<br> * Gray-cheeked Thrush (GCTH)<br> * Swainson's Thrush (SWTH)<br> * American Redstart (AMRE)<br> * Bay-breasted Warbler (BBWA)<br> * Black-throated Blue Warbler (BTBW)<br> * Canada Warbler (CAWA)<br> * Common Yellowthroat (COYE)<br> * Mourning Warbler (MOWA)<br> * Ovenbird (OVEN)</p> <p>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.</p> <p>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]:</p> <p>[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.</p> <p>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.</p> <p><br> Data Files<br> ------------<br> 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.</p> <p> </p> <p>Metadata Files<br> ---------------<br> `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.</p> <p><br> Please acknowledge BirdVox-ANAFCC in academic research<br> --------------------------------------------------------------------------</p> <p>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:</p> <p>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.</p> <p>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.</p> <p> </p> <p>Conditions of Use<br> ----------------------</p> <p>Dataset created by Aurora Cramer, Vincent Lostanlen, Bill Evans, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello.<br> <br> The BirdVox-ANAFCC dataset is offered free of charge under the terms of the Creative Commons Attribution International License:<br> https://creativecommons.org/licenses/by/4.0/<br> <br> 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.</p> <p><br> Feedback<br> -------------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and auroracramer@nyu.edu</p> <p>In case of a problem, please include as many details as possible.<br> <br> <br> Versions<br> ------------<br> 1.0, May 2020: initial version, paired with ICASSP 2020 publication.<br> 2.0, February 2022: added a missing dataset file (BirdVox-70k), updated name of first author (Aurora Cramer).<br> </p> <p><br> Acknowledgement<br> --------------------------<br> 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.</p> <p>We thank contributors and maintainers of the Macaulay Library and the Xeno-Canto website.</p> <p>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.</p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
BirdVox-70k: a dataset for species-agnostic flight call detection in half-second clips
<p>BirdVox-70k: a dataset for avian flight call detection in half-second clips<br> ======================================================================================<br> Version 1.0, April 2018.</p> <p><br> Created By<br> ----------</p> <p>Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3).</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Center for Urban Science and Progress, New York University<br> (3): Music and Audio Research Lab, New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> -----------</p> <p>The BirdVox-70k dataset contains 70k half-second clips from 6 audio recordings in the BirdVox-full-night dataset, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10.</p> <p>Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-70k, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.</p> <p>The dataset can be used, among other things, for the research,development and testing of bioacoustic classification mode ls, including the reproduction of the results reported in [1].</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[2] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p> <p> </p> <p>Data Files<br> ------------</p> <p>BirdVox-70k contains the recordings as HDF5 files, sampled at 24 kHz, with a single channel (mono). Each HDF5 file corresponds to a different sensor. The name of the HDF5 dataset in each file is "waveforms".</p> <p> </p> <p>Metadata Files<br> --------------</p> <p>Contrary to BirdVox-full-night, BirdVox-70k is not shipped with a metadata file. Rather, the metadata is included in the keys of the elements in the HDF5 files themselves, whose values are the waveforms.</p> <p>An example of BirdVox-70k key is:</p> <pre>unitID_TIMESTAMP_FREQ_LABEL </pre> <p>where</p> <ul> <li>ID is the identifier of the unit (01, 02, 03, 05, 07, or 10)</li> <li>TIMESTAMP is the timestamp of the center of the clip in the BirdVox-full-night recording. This timestamp is measured in samples at 24 kHz. It is accurate at about 10 ms.</li> <li>FREQ is the center frequency of the flight call, measured in Hertz. It is accurate at about 1 kHz. When the clip is negative, i.e. does not contain any flight call, it is set equal to zero by convention.</li> <li>LABEL is the label of the clip, positive (1) or negative (0).</li> </ul> <p> </p> <p>Example:</p> <pre>unit01_085256784_03636_1</pre> <p>is a positive clip in unit 01, with timestamp 085256784 (3552.37 seconds after dividing by the sample rate 24000), center frequency 3636 Hz.</p> <p> </p> <p>Another example:</p> <pre>unit05_284775340_00000_0</pre> <p>is a negative clip in unit 05, with timestamp 284775340 (11865.64 seconds).</p> <p> </p> <p>The approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) and UTC timestamps corresponding to the start of the recording for each sensor are included as CSV files in the main directory.</p> <p> </p> <p>Please acknowledge BirdVox-70k in academic research<br> ----------------------------------------------------------</p> <p>When BirdVox-70k is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.</p> <p>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.</p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello.</p> <p>The BirdVox-70k dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>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, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-70k dataset or any part of it.</p> <p> </p> <p>Feedback<br> -----------</p> <p>Please help us improve BirdVox-70k by sending your feedback to:<br> vincent.lostanlen@gmail.com and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> ----------------</p> <p>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.</p> <p>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.</p>
BirdVox-scaper-10k: a synthetic dataset for multilabel species classification of flight calls from 10-second audio recordings
<p>BirdVox-scaper-10k: a synthetic dataset for multilabel species classification of flight calls from 10-second audio recordings<br> =============================================================================================<br> Version 1.0, September 2019.</p> <p> </p> <p>Created By<br> -------------</p> <p>Elizabeth Mendoza (1), Vincent Lostanlen (2, 3, 4), Justin Salamon (3, 4), Andrew Farnsworth (2), Steve Kelling (2), and Juan Pablo Bello (3, 4).</p> <p> </p> <p>(1): Forest Hills High School, New York, NY, USA<br> (2): Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA<br> (3): Center for Urban Science and Progress, New York University, New York, NY, USA<br> (4): Music and Audio Research Lab, New York University, New York, NY, USA</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> --------------</p> <p>The BirdVox-scaper-10k dataset contains 9983 artificial soundscapes. Each soundscape lasts exactly ten seconds and contains one or several avian flight calls from up to 30 different species of New World warblers (Parulidae). Alongside each audio file, we include an annotation file describing the start time and end time of each flight call in the corresponding soundscape, as well as the species of warbler it belongs to.</p> <p>In order to synthesize soundscapes in BirdVox-scaper-10k, we mixed natural sounds from various pre-recorded sources. First, we extracted isolated recordings of flight calls containing little or no background noise from the CLO-43SD dataset [1]. Secondly, we extracted 10-second "empty" acoustic scenes from the BirdVox-DCASE-20k dataset [2]. These acoustic scenes contain various sources of real-world background noise, including biophony (insects) and anthropophony (vehicles), yet are guaranteed to be devoid of any flight calls. Lastly, we "fill" each acoustic scene by mixing it with flight calls sampled at random.</p> <p>Although the BirdVox-scaper-10k does not consist of natural recordings, we have taken several measures to ensure the plausibility of each synthesized soundscape, both from qualitative and quantitative standpoints.<br> <br> The BirdVox-scaper-10k dataset can be used, among other things, for the research, development, and testing of bioacoustic classification models.</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] J. Salamon, J. Bello. Fusing shallow and deep learning for bioacoustic bird species classification. Proc. IEEE ICASSP, 2017.</p> <p>[2] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, and J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[3] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p> </p> <p> </p> <p> </p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p>
Data from: A call in the dark: Nocturnal flight calls and their potential to advance the study of avian migration
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Magnolia Warbler (Setophaga magnolia) flight calls demonstrate individuality and variation by season and recording location
<p><span>Flight calls are short vocalizations frequently associated with migratory behavior that may maintain group structure, signal individual identity, and facilitate intra- and interspecific communication. In this study, Magnolia Warbler (<em>Setophaga magnolia</em>) flight call characteristics varied significantly by season and recording location, but not age or sex, and an individual's flight calls were significantly more similar to one another than to calls of other individuals. To determine if flight calls encode traits of the signaling individual during migration, we analyzed acoustic characteristics of the calls from the nocturnally migrating Magnolia Warbler. Specifically, we analyzed calls recorded from temporarily captured birds across the northeastern United States, including Appledore Island in Maine, Braddock Bay Bird Observatory in New York, and Powdermill Avian Research Center in Pennsylvania to quantify variation attributable to individual identity, sex, age, seasonality, and recording location. Overall, our findings suggest that Magnolia Warbler flight calls may show meaningful individual variation and exhibit previously undescribed spatiotemporal variation, providing a basis for future research.</span></p>
Magnolia Warbler (Setophaga magnolia) flight calls demonstrate individuality and variation by season and recording location
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Data from: Evaluation of methods to estimate nocturnal bird migration activity: A comparison of radar and nocturnal flight call monitoring in the American West
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Data and code from: Nocturnal flight call monitoring reveals in-flight behavioral alteration by avian migrants in response to artificial light at night
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Data from: Towards the automatic classification of avian flight calls for bioacoustic monitoring
Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.
Data from: Towards the automatic classification of avian flight calls for bioacoustic monitoring
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Data from: Flight calls signal group and individual identity but not kinship in a cooperatively breeding bird
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Data from: Nocturnal flight-calling behaviour predicts vulnerability to artificial light in migratory birds
Understanding interactions between biota and the built environment is increasingly important as human modification of the landscape expands in extent and intensity. For migratory birds, collisions with lighted structures are a major cause of mortality, but the mechanisms behind these collisions are poorly understood. Using 40 years of collision records of passerine birds, we investigated the importance of species' behavioral ecologies in predicting rates of building collisions during nocturnal migration through Chicago, IL and Cleveland, OH, USA. We found that use of nocturnal flight calls is an important predictor of collision risk in nocturnally migrating passerine birds. Species that produce flight calls during nocturnal migration collided with buildings more than expected given their local abundance, whereas those that do not use such communication collided much less frequently. Our results suggest that a stronger attraction response to artificial light at night in species that produce flight calls may mediate these differences in collision rates. Nocturnal flight calls likely evolved to facilitate collective decision-making during navigation, but this same social behavior may now exacerbate vulnerability to a widespread anthropogenic disturbance. Our results also suggest that social behavior during migration may reflect poorly-understood differences in navigational mechanisms across lineages of birds.
Data from: Nocturnal flight-calling behaviour predicts vulnerability to artificial light in migratory birds
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