High Performance Computer Acoustic Data Accelerator ( HPC-ADA ): A New System for Exploring Marine Mammal Acoustics for Big Data Applications

Peter J. Dugan, John Zollweg, Herve Glotin, Marian Popescu, Denise Risch, Yann Lecun, Christopher W. Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With continuing growth of the world's population and rapid economic development, our need to preserve the natural environment, especially our oceans, is becoming an increasing concern. For the past several decades scientists have been monitoring the oceans using a variety of sensors and tools. Passive acoustic monitoring is one of the primary methods used to investigate the behavior patterns of soniferous marine animals. Analyzing the vast amount of collected data poses an enormous challenge. This paper presents a new system designed for high speed acoustic processing called the High Performance Computer Acoustic Data Accelerator (HPC-ADA). Together with an appropriate software suite, the HPC-ADA is a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the HPC-ADA system’s architecture, software suite, and operation of the HPC-ADA. We also summarize the projects that have successfully used the HPC-ADA system; totaling over one million hours of processed sound to date.
Original languageEnglish
Title of host publicationProc. ICML Unsupervised learning for Bioacoustics
Pages1-8
Number of pages8
Volume1
Publication statusPublished - 2014

    Fingerprint

Keywords

  • - ocean acoustics
  • big data
  • data science
  • high performance computing
  • passive acoustic monitoring

Cite this

Dugan, P. J., Zollweg, J., Glotin, H., Popescu, M., Risch, D., Lecun, Y., & Clark, C. W. (2014). High Performance Computer Acoustic Data Accelerator ( HPC-ADA ): A New System for Exploring Marine Mammal Acoustics for Big Data Applications. In Proc. ICML Unsupervised learning for Bioacoustics (Vol. 1, pp. 1-8)