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T. Scott Brandes

Feature Vector Selection and Use With Hidden Markov Models to Identify Frequency-Modulated Bioacoustic Signals Amidst Noise. T. Scott Brandes. IEEE Transactions on Audio, Speech and Language Processing,2008. Outline. INTRODUCTION METHODS EXPERIMENTAL RESULTS AND DISCUSSION CONCLUSION.

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T. Scott Brandes

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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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