  Overarching goals for this work aim to advance the state of the art for
detection, classification and localization (DCL) in the field of bioacoustics.
This goal is primarily achieved by building a generic framework for
detection-classification (DC) using a fast, efficient and scalable
architecture, demonstrating the capabilities of this system using on a variety
of low-frequency mid-frequency cetacean sounds. Two primary goals are to
develop transferable technologies for detection and classification in, one: the
area of advanced algorithms, such as deep learning and other methods; and two:
advanced systems, capable of real-time and archival processing. For each key
area, we will focus on producing publications from this work and providing
tools and software to the community where/when possible. Currently massive
amounts of acoustic data are being collected by various institutions,
corporations and national defense agencies. The long-term goal is to provide
technical capability to analyze the data using automatic algorithms for (DC)
based on machine intelligence. The goal of the automation is to provide
effective and efficient mechanisms by which to process large acoustic datasets
for understanding the bioacoustic behaviors of marine mammals. This capability
will provide insights into the potential ecological impacts and influences of
anthropogenic ocean sounds. This work focuses on building technologies using a
maturity model based on DARPA 6.1 and 6.2 processes, for basic and applied
research, respectively.
