Phase 3: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Bioacoustic Applicaitons
Distributed, Parallel, and Cluster Computing2016-05-06v2
Goals of this research phase is to investigate advanced detection and classification pardims useful for data-mining passive large passive acoustic archives. Technical objectives are to develop and refine a High Performance Computing, Acoustic Data Accelerator (HPC-ADA) along with MATLAB based software based on time series acoustic signal Detection cLassification using Machine learning Algorithms, called DeLMA. Data scientists and biologists integrate to use the HPC-ADA and DeLMA technologies to explore data using newly developed techniques aimed at inspection of data extracted at large spatial and temporal scales.
@article{arxiv.1605.00983,
title = {Phase 3: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Bioacoustic Applicaitons},
author = {Peter J. Dugan and Christopher W. Clark and Yann André LeCun and Sofie M. Van Parijs},
journal= {arXiv preprint arXiv:1605.00983},
year = {2016}
}
Comments
National Oceanic Partnership Program (NOPP) sponsored by ONR and NFWF