Literature DB >> 15258662

Automated bioacoustic identification of species.

David Chesmore1.   

Abstract

Research into the automated identification of animals by bioacoustics is becoming more widespread mainly due to difficulties in carrying out manual surveys. This paper describes automated recognition of insects (Orthoptera) using time domain signal coding and artificial neural networks. Results of field recordings made in the UK in 2002 are presented which show that it is possible to accurately recognize 4 British Orthoptera species in natural conditions under high levels of interference. Work is under way to increase the number of species recognized.

Mesh:

Year:  2004        PMID: 15258662     DOI: 10.1590/s0001-37652004000200037

Source DB:  PubMed          Journal:  An Acad Bras Cienc        ISSN: 0001-3765            Impact factor:   1.753


  5 in total

1.  Infrared light sensors permit rapid recording of wingbeat frequency and bioacoustic species identification of mosquitoes.

Authors:  Dongmin Kim; Terry J DeBriere; Satish Cherukumalli; Gregory S White; Nathan D Burkett-Cadena
Journal:  Sci Rep       Date:  2021-05-11       Impact factor: 4.379

2.  Real-time bioacoustics monitoring and automated species identification.

Authors:  T Mitchell Aide; Carlos Corrada-Bravo; Marconi Campos-Cerqueira; Carlos Milan; Giovany Vega; Rafael Alvarez
Journal:  PeerJ       Date:  2013-07-16       Impact factor: 2.984

3.  Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring.

Authors:  Peter Prince; Andrew Hill; Evelyn Piña Covarrubias; Patrick Doncaster; Jake L Snaddon; Alex Rogers
Journal:  Sensors (Basel)       Date:  2019-01-29       Impact factor: 3.576

4.  Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

Authors:  Ranjana Jaiswara; Diptarup Nandi; Rohini Balakrishnan
Journal:  PLoS One       Date:  2013-09-25       Impact factor: 3.240

5.  Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab.

Authors:  Rodrigo Gurgel-Gonçalves; Ed Komp; Lindsay P Campbell; Ali Khalighifar; Jarrett Mellenbruch; Vagner José Mendonça; Hannah L Owens; Keynes de la Cruz Felix; A Townsend Peterson; Janine M Ramsey
Journal:  PeerJ       Date:  2017-04-18       Impact factor: 2.984

  5 in total

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