1. 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 1

2. Outline INTRODUCTION METHODS EXPERIMENTAL RESULTS AND DISCUSSION CONCLUSION 2

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 3

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 4

5. Introduction Basic shape variety and type of calls 5

6. Introduction 6

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 7

8. Methods Creating Frequency Bands 8

9. 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 9

10. Methods 10

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

12. Methods 12

13. Experimental Results and Discussion 13

14. Experimental Results and Discussion 14

15. Experimental Results and Discussion 15

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 16