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Advanced Signal Detection in Bioacoustic Signals Among Noise: A Hidden Markov Model Approach

This study presents an approach using Hidden Markov Models to identify frequency-modulated bioacoustic signals amidst noise. The goal is to automate the detection and classification of nonhuman natural sounds, crucial for various applications such as reducing bird-strikes and monitoring elusive species. Techniques like Dynamic Time Warping and Gaussian Mixture Models are employed to enhance signal detection accuracy in noisy environments, focusing on peak frequency measurements and formant extraction. Experimental results highlight the effectiveness of the proposed methods, particularly the use of composite HMMs for detecting higher-level call patterns.

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Advanced Signal Detection in Bioacoustic Signals Among Noise: A Hidden Markov Model Approach

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  1. Feature Vector Selection and Use With HiddenMarkov Models to Identify Frequency-ModulatedBioacoustic Signals Amidst Noise T. Scott Brandes IEEE Transactions on Audio, Speech and Language Processing,2008

  2. Outline • INTRODUCTION • METHODS • EXPERIMENTAL RESULTS AND DISCUSSION • CONCLUSION

  3. Introduction • A great need for automatic detection and classification of nonhuman natural sounds • Reduce bird-strikes by aircraft • Avoid bird-strikes of wind turbine generators • With the surge of interest in monitoring the effect of climate change • Monitor elusive species that can be indicators of habitat change • A range of techniques have been employed to detect sounds • Dynamic time warping • Hidden Markov models • Gaussian mixture models

  4. Introduction • Improve bioacoustic signal detection in the presence of noise • Measurements of the peak frequencies directly • Pitch determination algorithms • Spectral subbandcentroid and their histograms are used to extract peak frequency • Extract first three formants with Linear predictive coding coefficients

  5. Introduction Basic shape variety and type of calls

  6. Introduction

  7. Methods HMM Use With Automatic Call Recognition (ACR) • To find the call that maximizes the probability • In the model testing stage, the equation is maximized with a Viterbi search The conditional probability p is calculated for each state transition The conditional probability is calculated for each feature vector observed during that state transition

  8. Methods Creating Frequency Bands

  9. Methods Applying the ThresholdingFilter • A value greater than average value in that band are kept, and the others are set to zero Extracting Features for Each Event and Detecting Patterns With HMMs • Peak frequency • Short-time frequency bandwidth

  10. Methods

  11. Methods Using a Composite HMM to Detect Higher Level Patterns

  12. Methods

  13. Experimental Results and Discussion

  14. Experimental Results and Discussion

  15. Experimental Results and Discussion

  16. Conclusion • The performance of this process is most sensitive to the threshold-band filtering step • The contour feature vector used with the initial stage HMM is most effective • The sequence feature vector used with the second layer in the composite HMM is very effective at classifying sequences of calls

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