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17 results for “avian flight”
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-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>
Data from: A call in the dark: Nocturnal flight calls and their potential to advance the study of avian migration
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Data from: Phylogenetic and kinematic constraints on avian flight signals
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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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Adaptive morphing of wing and tail for stable, resilient, and energy-efficient flight of avian-informed drones
<p>This repository contains the code and data collected during the experiments described in the paper "Adaptive morphing of wing and tail for stable, resilient, and energy-efficient flight of avian-informed drones".</p> <p>The zip file has the following structure</p> <ul> <li>code (with its own README file)</li> <li>data (flight data of each experiment)</li> <li>video (video of each experiment)</li> </ul>
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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Landscape heterogeneity and novelty drive avian oscillatory flight behaviour during forebrain Wulst-Dependent visual map learning
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Data from: Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon
Birds primarily create and control the forces necessary for flight through changing the shape and orientation of their wings and tail. Their wing geometry is characterised by complex variation in parameters such as camber, twist, sweep and dihedral. To characterise this complexity, a multi-stereo photogrammetry setup was developed for accurately measuring surface geometry in high-resolution during free-flight. The natural patterning of the birds was used as the basis for phase correlation-based image matching, allowing indoor or outdoor use while being non-intrusive for the birds. The accuracy of the method was quantified and shown to be sufficient for characterising the geometric parameters of interest, but with a reduction in accuracy close to the wing edge and in some localized regions. To demonstrate the method's utility, surface reconstructions are presented for a barn owl (Tyto alba) and peregrine falcon (Falco peregrinus) during three instants of gliding flight per bird. The barn owl flew with a consistent geometry, with positive wing camber and longitudinal anhedral. Based on flight dynamics theory this suggests it was longitudinally statically unstable during these flights. The peregrine flew with a consistent glide angle, but at a range of airspeeds with varying geometry. Unlike the barn owl, its glide configuration did not provide a clear indication of longitudinal static stability/instability. Aspects of the geometries adopted by both birds appeared to be related to control corrections and this method would be well suited for future investigations in this area, as well as for other quantitative studies into avian flight dynamics.
Data from: Barb geometry of asymmetrical feathers reveals a transitional morphology in the evolution of avian flight
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Data from: Probabilistic divergence time estimation without branch lengths: dating the origins of dinosaurs, avian flight and crown birds
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Data from: Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon
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Data from:Inferring flight parameters of Mesozoic avians through multivariate analyses of forelimb elements in their living relatives
Our knowledge of the diversity, ecology, and phylogeny of Mesozoic birds has increased significantly during recent decades, yet our understanding of their flight competence remains poor. Wing loading (WL) and aspect ratio (AR) are two aerodynamically relevant parameters, as they relate to energy costs of aerial locomotion and flight maneuverability. They can be calculated in living birds (i.e., Neornithes) from body mass (BM), wingspan (B) and lift surface (SL). However, the estimates for extinct birds can be subject to biases from statistical issues, phylogeny, locomotor adaptations, and diagenetic compaction. Here we develop a sequential approach for generating reliable multivariate models that allow estimating measurements necessary to determine WL and AR in the main clades of non-neornithine Mesozoic birds. The strength of our predictions is supported by the use of those variables that show similar scaling patterns in modern and stem taxa (i.e., non-neornithine birds), and the similarity of our predictions with measurements obtained from fossils preserving wing outlines. In addition, although our WL and AR values are based on estimates (BM, B, and SL) that have an associated error, there is no cumulative error in their calculation, and both parameters show low prediction errors. Therefore we present the first taxonomically broad, error-calibrated estimation of these two important aerodynamic parameters in non-neornithine birds. Such estimates show that the WL and AR of the non-neornithine birds here analyzed fall within the range of variation of modern birds (i.e., Neornithes). Our results indicate that most modern flight modes (e.g., continuous flapping, flap and gliding, flap and bounding, thermal soaring) were possible for the wide range of non-neornithine avian taxa; we found no evidence for the presence of dynamic soaring among these early birds.
Datasets and scripts related to the manuscript "What makes the diverse flight of birds possible? Phylogenetic comparative analysis of avian alula morphology"
<p>"alula_data187.csv" and "alula_data162.csv" include the two duck sister species, Anas platyrhynchos and A. poecilorhyncha. The former lacks the migratory distance, while the latter contains the variable.</p> <p>"alula_data185.csv" and "alula_data160.csv" are similar to "alula_data187.csv" and "alula_data162.csv", respectively, but exclude the two duck species.</p> <p>"alula_data151.csv" and "alula_data130.csv" are similar to "alula_data185.csv" and "alula_data160.csv", respectively, but further exclude species based on female specimens.</p> <p>"consensus187.tre" is the consensus tree used for the phylogenetic comparative analyses.</p> <p>"scripts.txt" includes two R scripts for (1) calculating the migratory distance and (2) the phylogenetic comparative analyses.</p>
Data from:Inferring flight parameters of Mesozoic avians through multivariate analyses of forelimb elements in their living relatives
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Nonpathological inflammation drives the development of an avian flight adaptation
GEO Series GSE173884. Gallus gallus. 8 samples. Type: Expression profiling by high throughput sequencing.
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