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Feature Vector Selection and Use With HiddenMarkov Models to Identify Frequency-Modulated

Bioacoustic Signals Amidst Noise

T. Scott Brandes

IEEE Transactions on Audio, Speech and Language Processing,2008

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Outline

INTRODUCTION

METHODS

EXPERIMENTAL RESULTS AND DISCUSSION

CONCLUSION

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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

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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

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Introduction

Basic shape variety and type of calls

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Introduction

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MethodsHMM 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

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The conditional probability p is calculated for each state transition

The conditional probability is calculated for each feature vector observed during that state transition

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MethodsCreating Frequency Bands

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MethodsApplying 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

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Methods

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MethodsUsing a Composite HMM to Detect Higher Level Patterns

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Methods

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Experimental Results and Discussion

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Experimental Results and Discussion

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Experimental Results and Discussion

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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