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CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment

View ORCID ProfileA. J. Fairbrass, View ORCID ProfileM. Firman, C. Williams, View ORCID ProfileG. J. Brostow, View ORCID ProfileH. Titheridge, View ORCID ProfileK. E. Jones
doi: https://doi.org/10.1101/248708
A. J. Fairbrass
1Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
2Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
3Bat Conservation Trust, 5th floor, Quadrant House, 250 Kennington Lane, London, SE11 5RD, United Kingdom.
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  • For correspondence: alison.fairbrass.10@ucl.ac.uk michael.firman.10@ucl.ac.uk kate.e.jones@ucl.ac.uk
M. Firman
4Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
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  • For correspondence: alison.fairbrass.10@ucl.ac.uk michael.firman.10@ucl.ac.uk kate.e.jones@ucl.ac.uk
C. Williams
3Bat Conservation Trust, 5th floor, Quadrant House, 250 Kennington Lane, London, SE11 5RD, United Kingdom.
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G. J. Brostow
4Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
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H. Titheridge
1Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
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K. E. Jones
2Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
5Institute of Zoology, Zoological Society of London, Regent’s Park, London, NW1 4RY, United Kingdom.
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  • For correspondence: alison.fairbrass.10@ucl.ac.uk michael.firman.10@ucl.ac.uk kate.e.jones@ucl.ac.uk
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SUMMARY

  1. Cities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for biodiversity. However, existing algorithms for measuring biotic sound have been shown to be biased by non-biotic sounds in recordings, typical of urban environments.

  2. We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held-out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four acoustic indices (AIs): Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state-of-the-art bird call detection CNN (bulbul). We also compare the effect of non-biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient.

  3. CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, while the AIs performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real-world urban audio data.

  4. Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large-scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pre-trained models are made freely available in combination with two expert-annotated urban audio datasets to facilitate automated environmental surveillance in cities.

Footnotes

  • ↵† Denotes joint first authorship

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 16, 2018.
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CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment
A. J. Fairbrass, M. Firman, C. Williams, G. J. Brostow, H. Titheridge, K. E. Jones
bioRxiv 248708; doi: https://doi.org/10.1101/248708
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CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment
A. J. Fairbrass, M. Firman, C. Williams, G. J. Brostow, H. Titheridge, K. E. Jones
bioRxiv 248708; doi: https://doi.org/10.1101/248708

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