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

Abstract

Abstract : The overarching goals of this work are to advance the state of the art for detection, classification, and localization (DCL) in the field of bioacoustics. This goal is primarily achieved by building a generic framework for detection-classification (DC) using a fast, efficient, and scalable architecture, demonstrating the capabilities of the system using a variety of low-frequency and mid-frequency cetacean sounds. Two primary goals are to develop transferable technologies for detection and classification in the area of advanced algorithms, such as deep learning and other methods; and in advanced systems, capable of real-time and archival processing. Currently, massive amounts of acoustic data are being collected by various institutions, corporations, and national defense agencies. The long-term goal is to provide technical capability to analyze the data using automatic algorithms for DC based on machine intelligence. The goal of the automation is to provide effective and efficient mechanisms by which to process large acoustic datasets for understanding the bioacoustic behaviors of marine mammals. This capability will provide insights into the potential ecological impacts and influences of anthropogenic ocean sounds. From Oct 2012 through Sep 2013, our research focused on five major initiatives: (1) International workshops, conferences, and data challenges; (2) Enhancements of the Acoustic Segmentation Recognition (ASR) algorithm for frequency-modulated sounds: Right Whale Study; (3) Enhancements of the ASR algorithm for pulse trains: Minke Whale Study; (4) Mining Big Data Sound Archives using High Performance Computing software and hardware; and (5) Large Pulse Train Study: Minke Vocal Activity East Coast United States.