Christopher W. Clark

Peter Dugan, Melania Guerra, Dimitri Ponirakis et al.

This work presents a new toolkit for describing the acoustic properties of the ocean environment before, during and after a sound event caused by an underwater seismic air-gun. The toolkit uses existing sound measures, but uniquely applies these to capture the early time period (actual pulse) and late time period (reverberation and multiple arrivals). In total, 183 features are produced for each air-gun sound. This toolkit was utilized on data retrieved from a field deployment encompassing five marine autonomous recording units during a 46-day seismic air-gun survey in Baffin Bay, Greenland. Using this toolkit, a total of 147 million data points were identified from the Greenland deployment recordings. The feasibility of extracting a large number of features was then evaluated using two separate methods: a serial computer and a high performance system. Results indicate that data extraction performance took an estimated 216 hours for the serial system, and 18 hours for the high performance computer. This paper provides an analytical description of the new toolkit along with details for using it to identify relevant data.

Peter J. Dugan, Christopher W. Clark, Yann André LeCun et al.

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.

Marian Popescu, Peter J. Dugan, Mohammad Pourhomayoun et al.

The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been implemented to automatically detect and recognize pulse train songs of minke whales. While the long term goal of this work is to properly identify and detect minke songs from large multi-year datasets, this effort was developed using sounds off the coast of Massachusetts, in the Stellwagen Bank National Marine Sanctuary. The detection methodology is presented and evaluated on 232 continuous hours of acoustic recordings and a qualitative analysis of machine learning classifiers and their performance is described. The trained automatic detection and classification system is applied to 120 continuous hours, comprised of various challenges such as broadband and narrowband noises, low SNR, and other pulse train signatures. This automatic system achieves a TPR of 63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 score of 71%.