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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 subband centroid 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 • With HMMs, the probability of an observation sequence is given by Where A is the acoustic data P(A|C)The probability of capturing acoustic sequence A

  8. Methods

  9. Methods Creating Frequency Bands

  10. Methods Applying the Thresholding Filter • 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

  11. Methods

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

  13. Methods Managing the Process of Detection, Updating, and Classification

  14. Methods

  15. Experimental Results and Discussion

  16. Experimental Results and Discussion

  17. Experimental Results and Discussion

  18. 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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