Phase 3: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Bioacoustic Applicaitons

2016/05/03 by Peter J. Dugan, Christopher W. Clark, Dugan, Peter J. +6
Biochemistry, Genetics and Molecular Biology · Computer Science · Earth and Planetary Sciences · Environmental Science · #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

paper · pdf · doi:10.48550/arxiv.1605.00983

National Oceanic Partnership Program (NOPP) sponsored by ONR and NFWF

openalex publication_date 2016/05/03 · arxiv created 2016/05/05 · arxiv updated 2016/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Bioacoustical Periodic Pulse Train Signal Detection and Classification using Spectrogram Intensity Binarization and Energy Projection
• Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network

Related

• Phase 1: DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - HPC System Implementation
• Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications
• Phase 2: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Machine Learning Detection Algorithms
• High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications
• Soundbay: Deep Learning Framework for Marine Mammals and Bioacoustic Research