Peter J. Dugan

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  • Ira Rubinstein, David A Wheeler, Tony Stanco, Frank Petroski, John D Ramsdell, Bill Neugent +84 others
  • 2002
DISCLAIMER The views, opinions and/or findings contained in this report are those of The MITRE Corporation and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. This report documents the results of a short email-mediated study by The MITRE Corporation on the use of free and open-source(More)
 In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a(More)
 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(More)
 In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a(More)
This paper describes ongoing work being done at Cornell University to investigate the development of a complex system designed for extracting information from large acoustic datasets. The system, called DeLMA is based on integrating advanced machine learning with high performance computing (HPC). The goal of this work is to provide the capability to(More)
This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm-Acoustic Data-mining Accelerator (ADA)-is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together,(More)
BACKGROUND Little is known about migration patterns and seasonal distribution away from coastal summer feeding habitats of many pelagic baleen whales. Recently, large-scale passive acoustic monitoring networks have become available to explore migration patterns and identify critical habitats of these species. North Atlantic minke whales (Balaenoptera(More)
LONG TERM GOALS The goals of this project are [a] to develop a MATLAB toolbox, called Raven-X, intended for high performance data mining (detection and classification of target signals) of BIG acoustic datasets and [b] to make the toolbox freely available to the bioacoustic community. OBJECTIVES Our objective is to integrate high performance computing (HPC)(More)
In September and October 2011, a seismic survey took place in Baffin Bay, Western Greenland, in close proximity to a marine protected area (MPA). As part of the mitigation effort, five bottom-mounted marine acoustic recording units (MARUs) collected data that were used for the purpose of measuring temporal and spectral features from each impulsive event,(More)