Classifying Flies Based on Reconstructed Audio Signals

Flynn, Michael and Bagnall, Anthony (2019) Classifying Flies Based on Reconstructed Audio Signals. In: 20th International Conference on Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science . UNSPECIFIED.

[img] PDF (Classifying Flies Based on Reconstructed Audio Signals M. Flynn et al - REVIEWED) - Submitted Version
Restricted to Repository staff only until 18 October 2020.

Download (216kB) | Request a copy

Abstract

Advancements in sensor technology and processing power have made it possible to create recording equipment that can reconstruct the audio signal of insects passing through a directed infrared beam. The widespread deployment of such devices would allow for a range of applications previously not practical. A sensor net of detectors could be used to help model population dynamics, assess the efficiency of interventions and serve as an early warning system. At the core of any such system is a classification problem: given a segment of audio collected as something passes through a sensor, can we classify it? We examine the case of detecting the presence of fly species, with a particular focus on mosquitoes. This gives rise to a range of problems such as: can we discriminate between species of fly? Can we detect different species of mosquito? Can we detect the sex of the insect? Automated classification would significantly improve the effectiveness and efficiency of vector monitoring using these sensor nets. We assess a range of time series classification (TSC) algorithms on data from two projects working in this area. We assess our prior belief that spectral features are most effective, and we remark on all approaches with respect to whether they can be considered ``real-time''.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 13 Sep 2019 00:08
Last Modified: 10 Jun 2020 23:37
URI: https://ueaeprints.uea.ac.uk/id/eprint/72160
DOI: 10.1007/978-3-030-33617-2_26

Actions (login required)

View Item View Item