63urn:lsid:arphahub.com:pub:0E0032F4-55AE-5263-8B3C-F4DD637C30C2Biodiversity Information Science and StandardsBISS2535-0897Pensoft Publishers10.3897/biss.3.372273722711512Conference AbstractSS47 - Advancing biodiversity research through artificial intelligenceState of the Art in Computational Bioacoustics and Machine Learning: How far have we come?StowellDandan.stowell@qmul.ac.ukhttps://orcid.org/0000-0001-8068-37691Queen Mary University of London, London, United KingdomQueen Mary University of LondonLondonUnited Kingdom
Corresponding author: Dan Stowell (dan.stowell@qmul.ac.uk).
Academic editor:
2019190620193e37227891FD731-7983-5DA8-B6C2-484C118CA3AD325786612062019Dan StowellThis is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Terrestrial bioacoustics, like many other domains, has recently witnessed some transformative results from the application of deep learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et al. 2018, Mercado III and Sturdy 2017). Generalising over specific projects, which bioacoustic tasks can we consider "solved"? What can we expect in the near future, and what remains hard to do? What does a bioacoustician need to understand about deep learning? This contribution will address these questions, giving the audience a concise summary of recent developments and ways forward. It builds on recent projects and evaluation campaigns led by the author (Stowell et al. 2015, Stowell et al. 2018), as well as broader developments in signal processing, machine learning and bioacoustic applications of these. We will discuss which type of deep learning networks are appropriate for audio data, how to address zoological/ecological applications which often have few available data, and issues in integrating deep learning predictions with existing workflows in statistical ecology.
audiosoundmachine learningdeep learningbioacousticsEngineering and Physical Sciences Research Council501100000266http://doi.org/10.13039/5011000002662019Biodiversity_NextBiodiversity_Next 2019Leiden, The NetherlandsA joint conference by The Global Biodiversity Information Facility (GBIF), a new pan-European Research Infrastructure initiative (DiSSCo), the national resource for digitized information about vouchered natural history collections (iDigBio), Consortium of European Taxonomic Facilities (CETAF), Biodiversity Information Standards (TDWG) and LifeWatch ERIC, the e-Science and Technology European Infrastructure for Biodiversity and Ecosystem Research.Presenting author
Dan Stowell
Presented at
Biodiversity_Next 2019
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