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UID:submissions.supercomputing.org_SC22_sess274_rpost125@linklings.com
SUMMARY:Self-Supervised Learning for Automated Species Detection from Pass
 ively Recorded Soundscapes in Avian Diversity Monitoring
DESCRIPTION:Posters, Research Posters\n\nSelf-Supervised Learning for Auto
 mated Species Detection from Passively Recorded Soundscapes in Avian Diver
 sity Monitoring\n\nDematties, Raut, Sankaran, Ferrier\n\nBy detecting diff
 erent animal species reliably at scale we can protect biodiversity.  Yet, 
 traditionally, biodiversity data has been collected by expert observers wh
 ich is prohibitively expensive, not reliable neither scalable.  Automated 
 species detection via machine-learning is promising, but it is constrained
  by the necessity of large training data sets all labeled by human experts
 .  Here, we propose to use Self-Supervised Learning for studying semantic 
 features from passively collected acoustic data.  We utilized a joint embe
 dding configuration to acquire features from spectrograms.  We processed r
 ecordings from ∼190 hours of audio.  In order to process these volumes of 
 data we utilized a HPC cluster provided by the Argonne Leadership Computin
 g Facility.  We analyzed the output space from a trained backbone which hi
 ghlights important semantic attributes of the spectrograms.  We envisage t
 hese preliminary results as compelling for future automatic assistance of 
 biologist as a pre-processing stage for labeling very big data sets.\n\nRe
 gistration Category: Tech Program Reg Pass, Exhibits Reg Pass
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