2015/09/11 by Peter Dugan, Peter J. Dugan, John A. Zollweg +14 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Earth and Planetary Sciences · Environmental Science · #68-04 #Animal Vocal Communication and Behavior #Distributed #FOS: Computer and information sciences #Marine animal studies overview #Parallel #Underwater Acoustics Research #and Cluster Computing (cs.DC) #cs.DC #msc:68-04
paper · pdf · doi:10.48550/arxiv.1509.03591
Seven pages, submitted at International Conference on Machine Learning 2014, Workshop uLearnBio, unsupervised learning for bioacoustic applications
arxiv created 2015/09/11 · openalex publication_date 2015/09/11 · arxiv updated 2015/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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, DeLMA and the ADA algorithm provide 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 system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.