Detecting and reducing heterogeneity of error in acoustic classification-Reference-Cited by-同舟云学术

Detecting and reducing heterogeneity of error in acoustic classification

Published:2022-08-31 Issue:11 Volume:13 Page:2559-2571
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Metcalf Oliver C.¹^ORCID,Barlow Jos²^ORCID,Bas Yves³⁴,Berenguer Erika³⁵^ORCID,Devenish Christian¹⁶^ORCID,França Filipe⁷^ORCID,Marsden Stuart¹^ORCID,Smith Charlotte³^ORCID,Lees Alexander C.¹⁸^ORCID

Affiliation:

1. Division of Biology and Conservation Ecology, Department of Natural Sciences Manchester Metropolitan University Manchester UK

2. Lancaster Environment Centre Lancaster University Lancaster UK

3. CESCO, MNHN, CNRS, Sorbonne Univ Paris France

4. CEFE, Univ Montpellier, CNRS, EPHE, IRD Montpellier France

5. Environmental Change Institute University of Oxford Oxford UK

6. Nature Metrics, Surrey Research Park Guildford UK

7. School of Biological Sciences University of Bristol Bristol UK

8. Cornell Lab of Ornithology Cornell University Ithaca New York USA

Publisher

Wiley

Subject

Ecological Modeling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13967

Reference47 articles.

1. Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring

2. Tadarida: A Toolbox for Animal Detection on Acoustic Recordings

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4. Detecting and reducing heterogeneity of error in acoustic classification;Methods in Ecology and Evolution;2022-08-31