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Automatic fish sounds classification.

Marielle Malfante1, Jérôme I Mars1, Mauro Dalla Mura1

  • 1Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France.

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Summary

This study introduces an advanced acoustic system for monitoring ocean vitality and fish populations. The new method significantly improves the accuracy of detecting and classifying fish sounds using machine learning.

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Area of Science:

  • Marine Biology
  • Acoustic Oceanography
  • Signal Processing

Background:

  • Passive acoustic monitoring (PAM) is crucial for assessing ocean vitality and fish population dynamics.
  • Traditional methods for analyzing underwater acoustic data often lack the precision needed for detailed population studies.
  • Identifying and classifying fish sounds is essential for understanding marine ecosystem health.

Purpose of the Study:

  • To develop and validate a novel acoustic system for passive acoustic monitoring of fish populations.
  • To enhance the accuracy and reliability of fish sound detection and classification in marine environments.
  • To demonstrate the system's effectiveness in operational scenarios for real-time monitoring.

Main Methods:

  • Utilized supervised machine learning, specifically random-forest and support-vector machines, to build a discriminative classification model.
  • Extracted features from time, frequency, and cepstral domains, drawing from established signal processing techniques.
  • Represented acoustic acquisitions in a feature space designed to maximize separability between different semantic classes.

Main Results:

  • Achieved a 96.9% correct classification rate for fish sounds, a significant improvement over the 72.5% accuracy of existing state-of-the-art features.
  • Validated the classification scheme on real fish sounds recorded in various marine areas.
  • Demonstrated successful detection and classification of fish sounds in continuous underwater recordings, proving operational viability.

Conclusions:

  • The proposed acoustic system and feature extraction method offer a highly accurate and robust solution for passive acoustic monitoring of fish populations.
  • This technology can be effectively deployed in operational scenarios for continuous, real-time assessment of marine ecosystem health.
  • The findings highlight the potential of advanced machine learning techniques in advancing underwater acoustic research and conservation efforts.