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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