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Automatic data selection for validation: A method to determine cetacean occurrence in large acoustic data sets.

Katie A Kowarski1, Julien J-Y Delarue1, Briand J Gaudet1

  • 1JASCO Applied Sciences, 32 Troop Avenue, Suite 202, Dartmouth, Nova Scotia B3B 1Z1, Canada katie.kowarski@jasco.com, julien.delarue@jasco.com, briand.gaudet@jasco.com, bruce.martin@jasco.com.

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|September 26, 2022
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Summary
This summary is machine-generated.

Passive acoustic monitoring (PAM) effectively identifies cetacean species using an automatic data selection for validation (ADSV) method. This approach matches manual review results, improving large dataset analysis for wildlife management.

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Area of Science:

  • Marine biology
  • Bioacoustics
  • Wildlife management

Background:

  • Passive acoustic monitoring (PAM) is crucial for understanding cetacean distribution.
  • Analyzing large PAM datasets manually is time-consuming and resource-intensive.
  • Efficient methods are needed to process extensive acoustic data for wildlife management applications.

Purpose of the Study:

  • To present an automatic data selection for validation (ADSV) method for analyzing large PAM datasets.
  • To enable effective identification of all acoustically present cetacean species.
  • To compare the efficacy of the ADSV method against traditional manual review techniques.

Main Methods:

  • Development and application of automated acoustic detectors for cetacean vocalizations.
  • Automated selection of data subsets for targeted manual review and validation.
  • Evaluation and optimization of automated detector performance using selected data.

Main Results:

  • The ADSV method successfully identified acoustically present cetacean species in large datasets.
  • Comparison with systematic manual review showed similar species occurrence results.
  • Hourly occurrence matching between the ADSV method and manual review ranged from 73% to 100%.

Conclusions:

  • The ADSV method provides an effective and efficient approach for analyzing large passive acoustic monitoring datasets.
  • This automated method can significantly aid in determining cetacean distribution for wildlife management.
  • ADSV offers comparable results to intensive manual reviews, optimizing resource allocation.