The Cornell University Bioacoustics Research Program (BRP) is collecting terabytes of ocean acoustic data containing the sounds of large baleen whales and other marine mammals in order to understand how human activities affect the ocean’s acoustic ecosystem. A data set that would have taken months to process can now be processed multiple times in just a few days using different detection algorithms.

This session describes how BRP data scientists use MATLAB^® to develop high-performance computing software to process and analyze terabytes of acoustic data. This includes the use of signal and image processing algorithms and machine learning techniques to detect and classify animal signals in the presence of various levels of background noise, much of it from commercial shipping and seismic airgun surveys prospecting for offshore oil and gas.

To evaluate the algorithm accuracy, BRP scientists used statistical tools to compute a suite of performance curves. After optimizing the algorithms on small data sets, they ran them against several full archived data sets on a 64-node cluster. BRP also collaborated with Marinexplore and the Kaggle community to sponsor a worldwide competition in which more than 240 participants submitted algorithms for detection and classification. They are identifying the most accurate algorithms to use in helping reduce lethal ship collisions with whales and the massive increases in ocean noise from human activities.

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