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Bird sound recognition based on adaptive frequency cepstral coefficient and improved support vector machine using a

Xiao Chen1,2, Zhaoyou Zeng1

  • 1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Mathematical Biosciences and Engineering : MBE
|December 5, 2023
PubMed
Summary

Accurate bird sound recognition aids conservation efforts. A new machine learning method enhances bird sound classification accuracy using adaptive frequency cepstrum coefficients and an optimized support vector machine model.

Keywords:
adaptive frequency cepstral coefficientsaudio signal processingbioacousticsbird sound recognitionhunter-prey optimizermachine learningsupport vector machine

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Area of Science:

  • Bioacoustics
  • Machine Learning
  • Computational Ecology

Background:

  • Bird populations are declining rapidly, necessitating effective monitoring for conservation.
  • Accurate bird sound recognition is vital for assessing biodiversity and environmental adaptation.
  • Existing machine learning models require improvement for reliable performance in low-cost systems.

Purpose of the Study:

  • To develop an improved machine learning model for accurate bird sound recognition.
  • To enhance feature extraction using adaptive frequency cepstrum coefficients.
  • To optimize support vector machine performance with a hunter-prey optimizer algorithm.

Main Methods:

  • Introduced an adaptive factor into frequency cepstrum coefficient extraction to adjust filter characteristics.
  • Extracted full-band frequency features by combining two filter groups.
  • Employed a hunter-prey optimizer to enhance a support vector machine classification model.

Main Results:

  • Achieved a 93.45% recognition accuracy for five bird sound types.
  • Demonstrated superior performance compared to state-of-the-art support vector machine models.
  • Identified optimal adaptive factor values for maximizing recognition accuracy.

Conclusions:

  • The proposed method significantly improves bird sound recognition accuracy.
  • Adaptive feature extraction and optimized machine learning enhance classification performance.
  • This approach offers a valuable tool for bird monitoring and conservation applications.