CityNet—Deep learning tools for urban ecoacoustic assessment-Reference-Cited by-同舟云学术

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CityNet—Deep learning tools for urban ecoacoustic assessment

Published:2018-11-26 Issue:2 Volume:10 Page:186-197
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Fairbrass Alison J.¹²³^ORCID,Firman Michael⁴,Williams Carol³,Brostow Gabriel J.⁴^ORCID,Titheridge Helena¹^ORCID,Jones Kate E.²⁵^ORCID

Affiliation:

1. Department of Civil, Environmental and Geomatic EngineeringCentre for Urban Sustainability and ResilienceUniversity College London London UK

2. Department of Genetics, Evolution and EnvironmentCentre for Biodiversity and Environment ResearchUniversity College London London UK

3. Bat Conservation Trust London UK

4. Department of Computer ScienceUniversity College London London UK

5. Zoological Society of LondonInstitute of Zoology London UK

Funder

Engineering and Physical Sciences Research Council

Publisher

Wiley

Subject

Ecological Modelling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13114

Reference55 articles.

1. Automated classification of bird and amphibian calls using machine learning: A comparison of methods

2. Real-time bioacoustics monitoring and automated species identification

3. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers

4. Biodiversity in cities needs space: a meta-analysis of factors determining intra-urban biodiversity variation

5. MULTI-TROPHIC INVASION RESISTANCE IN HAWAII: BIOACOUSTICS, FIELD SURVEYS, AND AIRBORNE REMOTE SENSING

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5. Leveraging time-based acoustic patterns for ecosystem analysis;Neural Computing and Applications;2024-08-13

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