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Related Experiment Video

Updated: Jun 2, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

A generalized baleen whale call detection and classification system.

Mark F Baumgartner¹, Sarah E Mussoline

¹Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA. mbaumgartner@whoi.edu

The Journal of the Acoustical Society of America

|May 17, 2011

Summary

This summary is machine-generated.

A new automated system accurately detects and classifies marine mammal sounds, overcoming manual analysis limitations. This technology enhances passive acoustic monitoring for whale distribution and occurrence studies.

Related Experiment Videos

Last Updated: Jun 2, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

Area of Science:

Marine biology
Bioacoustics
Acoustic signal processing

Background:

Passive acoustic monitoring (PAM) offers broad-scale marine mammal assessment.
Manual analysis of large acoustic datasets is time-consuming and labor-intensive.
Automated methods are needed to overcome limitations in PAM data analysis.

Purpose of the Study:

To develop and validate a generalized automated detection and classification system (DCS) for low-frequency baleen whale calls.
To improve the efficiency and accuracy of analyzing passive acoustic monitoring data.
To facilitate broader application of PAM in marine mammal research.

Main Methods:

Developed a generalized automated detection and classification system (DCS).
The DCS incorporates noise reduction, pitch-tracking for frequency variation, and quadratic discriminant function analysis (QDFA) for classification.
Applied the DCS to recordings of sei whale and North Atlantic right whale calls from the Gulf of Maine (2006-2007).

Main Results:

The DCS demonstrated accuracy comparable to human analysts in detecting and classifying whale calls.
Variability in DCS performance was similar to inter-analyst variability.
The system accurately captured temporal variations in whale call rates.

Conclusions:

The automated DCS effectively identifies and classifies baleen whale vocalizations.
This system significantly enhances the efficiency and accuracy of passive acoustic monitoring data analysis.
The DCS is a valuable tool for large-scale marine mammal occurrence and distribution studies.