Topic 1. Marine Mammal Detection and Monitoring

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-time Recognition and Localization of Marine Mammals

Lead PI: Dr. Christopher Clark, Cornell University

The primary goals of the proposed collaborative project are to: (1) to apply and study advanced new approaches in machine learning (i.e., the convolution neural net technology, ConvNet) to bring detection and classification of marine mammals to an entirely new level of performance, (2) leverage past experiences and advanced computing to develop enhanced real-time algorithms for locating marine mammals, (3) overcome the limitations of current state-of-the-art technologies by applying, evaluating and studying a systems approach that includes the integration of the ConvNet technology with Cornell’s acoustic array processing system, Minatour, and converts these into an open and extensible real-time DCL system that can be used in a variety of environments, and (4) demonstrate the accuracy and precision of the DCL system by merging this system with a state-of-the-art towed beamforming array.

Number of Years: 3

Requested Funds: $1,048,861

Partners:

New York University
Cornell University
Pacific Northwest National Laboratory
Lockheed Martin
Hydroscience Technologies, Inc.

Portable and Persistent Autonomous Real-time Marine Mammal Acoustic Monitoring

Lead PI: Dr. Christopher Clark, Cornell University

The proposed project objectives are: (1) Develop a real-time, portable, Marine Mammal Monitoring System, (2) Acquire marine acoustic data using a four-channel hydrophone array towed behind an SAIC WaveGlider, with BRP’s Pop-Up electronics integrated onto the WaveGlider, (3) Transmit selected acoustic data and associated metadata in real-time to the DMAC receiver via the SAIC high-speed satellite data link, (4)Optimize detection, classification, and localization (DCL) operations both on-board the WaveGlider and at the DMAC receiver to achieve real-time performance similar to BRP’s
current Auto-Buoy system and Pop-Up post-processing algorithms. These objectives will be achieved by demonstrating the feasibility of integrating the Pop-Up electronics and Satcom with the WaveGlider, then expanding existing DMAC capability by leveraging Auto-Buoy development, and finally implementing detection algorithms on-board the WaveGlider platform along with bearing estimates in the DMAC for real-time display of marine mammal vocalizations. Such a system would be capable of persistent, autonomous, real-time monitoring of marine mammals in areas that would otherwise not be surveyed, as it will not require a local ship for its deployment, its retrieval, or reception of data for human review.

Number of Years: 3

Requested Funds: $1,049,818

Partners:

Cornell University
Scientific Applications International Corporation (SAIC)

Expansion of Metadata Management, Visualization, and Data Processing Functionality of OBIS-SEAMAP

Lead PI: Dr. Patrick Halpin, Duke University

To develop the functionality required to serve as a central passive acoustics data portal, we are envisioning a framework for passive acoustic monitoring data in three tiers or levels (Figure 1). Currently OBIS-SEAMAP has focused only on the development of prototype tools for the archival and dissemination of fully processed and published marine mammal localization products. This final class of passive acoustics products would represent the most complete form or Tier 3 data in our proposed hierarchy. The core aim of this proposal is to develop a more seamless system for presenting data across multiple levels of processing and development.

Number of Years: 2

Requested Funds: $317,993

Partners:

Duke University
Cornell University
San Diego State University
NOAA Northeast Fisheries Science Center

Instantaneous Passive and Active Detection, Localization and Monitoring of Marine Mammals over Long Ranges

Lead PI: Dr. Purnima Ratilal, Northeastern University

The goal of this proposal is to develop approaches to enable instantaneous passive and active acoustic detection, localization and continuous monitoring of marine mammals over very wide areas spanning hundreds of kilometers or more in range. This will be accomplished using high resolution receiver array measurements of marine mammal vocalizations (passive) and instantaneous wide-area ocean acoustic waveguide remote sensing (OAWRS) and imaging (active) of marine mammals.

Number of Years: 3

Requested Funds: $431,978

Partners:

Northeastern University
Massachusetts Institute of Technology
BAE Systems
Woods Hole Oceanographic Institution
Defence Science and Technology Organisation
Applied Physical Sciences

Acoustic Metadata Management and Transparent Access to Networked Oceanographic Data Sets

Lead PI: Dr. Marie Roch, San Diego State University

The researchers propose to create a database system that can be used by marine mammal researchers, governments, and private industry to organize and share passive acoustic detections of marine mammals. This work is driven by the need to understand marine mammal ecology and behavior as well as the impacts of anthropogenic activities. Passive acoustic methods offer an increasingly effective technique for the monitoring of marine mammals. Advances in passive acoustic monitoring technology have produced a wealth of high sample-rate acoustic data, with multiple terabyte data sets becoming a standard practice. Analysis of these data have traditionally been accomplished through human scanning, with automated methods gaining ground in the last decade.

Number of Years: 4

Requested Funds: $855,499

Partners: