Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity

Authors

  • Forrest Briggs Facebook, Inc.
  • Xiaoli Fern Oregon State University
  • Raviv Raich Oregon State University
  • Matthew Betts Oregon State University

DOI:

https://doi.org/10.1609/aaai.v30i1.9907

Abstract

We study the problem of analyzing a large volume ofbioacoustic data collected in-situ with the goal of assessingthe biodiversity of bird species at the data collectionsite. We are interested in the class discoveryproblem for this setting. Specifically, given a large collectionof audio recordings containing bird and othersounds, we aim to automatically select a fixed size subsetof the recordings for human expert labeling suchthat the maximum number of species/classes is discovered.We employ a multi-instance multi-label representationto address multiple simultaneously vocalizingbirds with sounds that overlap in time, and proposenew algorithms for species/class discovery using thisrepresentation. In a comparative study, we show that theproposed methods discover more species/classes thancurrent state-of-the-art in a real world datasetof 92,095 ten-second recordings collected in field conditions.

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Published

2016-03-05

How to Cite

Briggs, F., Fern, X., Raich, R., & Betts, M. (2016). Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9907

Issue

Section

Special Track: Computational Sustainability