A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2016
- Type
- D - Journal article
- DOI
-
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
-
-
- 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
-
3
- Research group(s)
-
-
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes the first use of acoustic detection algorithms, deployed in a smartphone app, to perform a large-scale citizen scientist search for a rare insect. The smartphone app, CicadaHunt, has been downloaded over 6,000 times, and over 6,000 reports were generated from the citizen science campaigns in the UK during 2013 and 2014. The work subsequently led to the development of a stand-alone smart acoustic sensor, called AudioMoth, of which over 10,000 are being used by conservation researchers worldwide. A spin-out company co-founded by Rogers, Open Conservation Ltd, continues to support and distribute this device.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
