  While the animal bioacoustics community at large is collecting huge amounts
of acoustic data at an unprecedented pace, processing these data is
problematic. Currently in bioacoustics, there is no effective way to achieve
high performance computing using commericial off the shelf (COTS) or government
off the shelf (GOTS) tools. Although several advances have been made in the
open source and commercial software community, these offerings either support
specific applications that do not integrate well with data formats in
bioacoustics or they are too general. Furthermore, complex algorithms that use
deep learning strategies require special considerations, such as very large
libraiers of exemplars (whale sounds) readily available for algorithm training
and testing. Detection-classification for passive acoustics is a data-mining
strategy and our goals are aligned with best practices that appeal to the
general data mining and machine learning communities where the problem of
processing large data is common. Therefore, the objective of this work is to
advance the state-of-the art for data-mining large passive acoustic datasets as
they pertain to bioacoustics. With this basic deficiency recognized at the
forefront, portions of the grant were dedicated to fostering deep-learning by
way of international competitions (kaggle.com) meant to attract deep-learning
solutions. The focus of this early work was targeted to make significant
progress in addressing big data systems and advanced algorithms over the
duration of the grant from 2012 to 2015. This early work provided simulataneous
advances in systems-algorithms research while supporting various collaborations
and projects.
