For more than 30 years, scientists have studied local animal populations by recording animal sounds in oceans, jungles, forests, and other natural environments. They use the results to assess the effect of man-made noise on natural environments, monitor endangered animal populations, and investigate animal communication. Passive acoustic monitoring systems record sounds continuously, generating terabytes of data. Scientists are often unable to process even 1% of this data because they lack the necessary advanced algorithms and processing capacity.
Bioacoustics Research Program (BRP) scientists at the Cornell Laboratory of Ornithology analyze vast amounts of acoustic data with MATLAB®, Parallel Computing Toolbox™, and MATLAB Distributed Computing Server™. The project, funded by a grant from the Office of Naval Research and the National Oceanic Partnership Program, is led by two principal investigators from Cornell: Dr. Christopher Clark, senior scientist and director of BRP, and Dr. Peter Dugan, lead data scientist for BRP.
“MATLAB and MATLAB parallel computing tools gave us the flexibility to dynamically improve and adapt the algorithms that we use to process our big acoustic data sets,” says Dr. Clark. “If we were using C++ or a similar language, we would not be able to move as quickly or explore as many scenarios.”