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

TL;DR

This paper presents a scalable deep learning framework for real-time detection, classification, and localization of marine mammals using bioacoustic data, aiming to enhance analysis of large datasets and ecological insights.

Contribution

It introduces a generic, efficient deep learning-based system for marine mammal bioacoustic detection and localization, capable of real-time processing and adaptable to various sound types.

Findings

Demonstrated system effectiveness on diverse cetacean sounds

Achieved real-time detection and classification capabilities

Provided tools and software for community use

Abstract

Overarching goals for this work aim 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 this system using on a variety of low-frequency mid-frequency cetacean sounds. Two primary goals are to develop transferable technologies for detection and classification in, one: the area of advanced algorithms, such as deep learning and other methods; and two: advanced systems, capable of real-time and archival processing. For each key area, we will focus on producing publications from this work and providing tools and software to the community where/when possible. 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. This work focuses on building technologies using a maturity model based on DARPA 6.1 and 6.2 processes, for basic and applied research, respectively.