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Enregistrement W6931071409 · doi:10.5281/zenodo.3667093

BirdVox-14SD: a dataset of flight calls with species annotation

2020· article· en· W6931071409 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueSoil erosion and sediment transport
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNoise (video)Context (archaeology)Feature (linguistics)LimitingTerm (time)Matching (statistics)

Résumé

récupéré en direct d'OpenAlex

BirdVox 14 Species Dataset (BirdVox-14SD) ============= Version 1.0, May 2020. Created By ---------- Vincent Lostanlen (1, 2, 3), Andrew Farnsworth (1), Jason Cramer (2, 3), and Juan Pablo Bello (2, 3). (1): Cornell Lab of Ornithology (CLO) (2): Center for Urban Science and Progress, New York University (3): Music and Audio Research Lab, New York University https://wp.nyu.edu/birdvox Description ----------- The BirdVox 14 Species Dataset (BirdVox-14SD) contains 14,336 audio clips of avian flight calls, each ranging from about 150 ms to 500 ms in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the 2015 migration season (August - November). Nine different sensors were used, originally numbered 1, 2, 3, 4, 5, 6, 7, 8, and 10. These sensors acquired audio recordings in intervals of two hours across the season. A subsample of 150 these two-hour recordings were chosen for annotation, using the Entrofy library [3] in order to maximize diversity across sensor locations, time of day, week in the season, and background noise characteristics (as represented by vector quantizations of median MFCCs). Andrew Farnsworth used the Raven software to pinpoint every avian flight call and labeled the corresponding order, family, and species. The dataset can be used, among other things, for the research, development and testing of bioacoustic classification models, including the reproduction of the results reported in [1]. For details on the hardware of ROBIN recording units, we refer the reader to [2]. [1] J. Cramer, V. Lostanlen, A. Farnsworth, J. Salamon, J.P. 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. [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. [3] D. Huppenkothen, B. McFee, L. Norén. Entrofy Your Cohort: A Data Science Approach to Candidate Selection. PLoS One, 2020. 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.\*.\* - Passerine * Family * 1.1.\* - American Sparrow * 1.2.\* - Cardinals * 1.3.\* - Thrushes * 1.4.\* - New World warblers * Species * 1.1.1 - American tree sparrows (ATSP) * 1.1.2 - Chipping sparrow (CHSP) * 1.1.3 - Savannah sparrow (SAVS) * 1.1.4 - White-throated sparrow (WTSP) * 1.2.1 - Rose-breasted grosbeak (RBGR) * 1.3.1 - Gray-cheeked thrush (GCTH) * 1.3.2 - Swainson's thrush (SWTH) * 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) 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. The file `taxonomy.yaml` details this taxonomy structure. Data Files ------------ BirdVox-14SD 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-14SD_ _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 - _ _ `. Please acknowledge BirdVox-14SD in academic research -------------------------------------------------------------------------- When BirdVox-14SD is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication: J. Cramer, V. Lostanlen, A. Farnsworth, J. Salamon, J.P. 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 1633259 (BIRDVOX). Conditions of Use ---------------------- Dataset created by Vincent Lostanlen, Andrew Farnsworth, Jason Cramer, Justin Salamon, and Juan Pablo Bello. The BirdVox-14SD 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-14SD dataset or any part of it. Feedback ----------- Please help us improve BirdVox-14SD by sending your feedback to: vincent.lostanlen@gmail.com and jtcramer@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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,072

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0020,002
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0210,040

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,057
Tête enseignante GPT0,210
Écart entre enseignants0,153 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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