Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity
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.
Cite
Text
Briggs et al. "Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9907Markdown
[Briggs et al. "Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/briggs2016aaai-multi/) doi:10.1609/AAAI.V30I1.9907BibTeX
@inproceedings{briggs2016aaai-multi,
title = {{Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity}},
author = {Briggs, Forrest and Fern, Xiaoli Z. and Raich, Raviv and Betts, Matthew},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {3807-3813},
doi = {10.1609/AAAI.V30I1.9907},
url = {https://mlanthology.org/aaai/2016/briggs2016aaai-multi/}
}