  Many biological monitoring projects rely on acoustic detection of birds.
Despite increasingly large datasets, this detection is often manual or
semi-automatic, requiring manual tuning/postprocessing. We review the state of
the art in automatic bird sound detection, and identify a widespread need for
tuning-free and species-agnostic approaches. We introduce new datasets and an
IEEE research challenge to address this need, to make possible the development
of fully automatic algorithms for bird sound detection.
