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Animal Species Classification from Vocalizations Using Cochlear-Inspired Audio Features and Machine Learning.

Karim Youssef1, Julien Moussa H Barakat1, Ghina El Mir2

  • 1College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.

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Summary
This summary is machine-generated.

This study introduces a biomimetic approach using gammatone filtering, inspired by the human cochlea, for animal species classification. The method shows promise for robust sound-based species identification using artificial neural networks.

Keywords:
auditory modelingcochlear filteringmachine learningsound signal processingspecies classification

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Area of Science:

  • Bioacoustics
  • Computational Auditory Scene Analysis
  • Machine Learning

Background:

  • Biomimetic approaches are increasingly used for computational sound analysis.
  • The human cochlea's auditory processing mechanisms offer a model for signal processing.

Purpose of the Study:

  • To develop and evaluate a sound-based animal species classification method inspired by human cochlear processing.
  • To explore gammatone filtering for extracting distinctive features from animal vocalizations.

Main Methods:

  • Employed gammatone filtering to create filterbanks mimicking the human cochlea.
  • Extracted four distinct gammatone-based feature representations.
  • Trained and tested an artificial neural network (ANN) for species classification.
  • Evaluated the system on a dataset of 13 animal species' vocalizations.

Main Results:

  • The study systematically applied and evaluated gammatone filterbanks for animal vocalization classification.
  • Results demonstrated the potential of combining biomimetic cochlear filtering with ANNs.
  • The approach showed promise for reliable and robust species classification through sound analysis.

Conclusions:

  • Biomimetic cochlear filtering combined with machine learning offers a viable strategy for species identification.
  • The proposed method highlights the effectiveness of gammatone features in capturing animal vocalization characteristics.
  • Further research can explore larger datasets and refined system architectures for enhanced performance.