A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring
- Submitting institution
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University of Southampton
- Unit of assessment
- 12 - Engineering
- Output identifier
- 20808264
- Type
- D - Journal article
- DOI
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10.1613/jair.4434
- Title of journal
- Journal of Artificial Intelligence Research
- Article number
- -
- First page
- 805
- Volume
- 51
- Issue
- -
- ISSN
- 1076-9757
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This interdisciplinary paper presents an algorithm for bioacoustic detection of the UK's last native cicada, which can operates on a constrained smartphone. The paper is an extended version of a Best Paper from the IJCAI’13 conference. The citizen science actively engaged with >12,000 people (total app downloads; 5,000+ on Android http://bit.ly/30ZD9Ju), submitting 23,000 measurements (>4,000 in 2013 alone; http://bit.ly/2QDnkDv). Societal impact evidenced through public engagement (London Zoo, BioBlitz, British Science Festival; http://bit.ly/2XlC4JW) and press (BBC https://bbc.in/2Khumg8, New Scientist: http://bit.ly/30X7597). The research led to a project funded by the Google Impact Award and Gates Foundation, using smartphones to detect mosquitos (http://bit.ly/342NMvt).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -