feature subset selection for automatically classifying anuran calls using sensor networks
DESCRIPTION
Anurans (frogs or toads) are commonly used by biologists as early indicators of ecological stress. The reason is that anurans are closely related to the ecosystem. Although several sources of data may be used for monitoring these animals, anuran calls lead to a non-intrusive data acquisition strategy. Moreover, wireless sensor networks (WSNs) may be used for such a task, resulting in more accurate and autonomous system. However, it is essential save resources to extend the network lifetime. In this paper, we evaluate the impact of reducing data dimension for automatic classification of bioacoustic signals when a WSN is involved. Such a reduction is achieved through a wrapper-based feature subset selection strategy that uses genetic algorithm (GA). We use GA to find the subset of features that maximizes the cost-benefit ratio. In addition, we evaluate the impact of reducing the original feature space, when sampling frequencies are also reduced. Experimental results indicate that we can reduce the number of features, while increasing classification rates (even when smaller sampling frequencies of transmission are used).TRANSCRIPT
Feature Subset Selection for Automatically Classifying Anuran Calls Using Sensor Networks
Juan G. ColonnaAfonso D. RibasEduardo F. NakamuraEulanda M. dos Santos
Institute of Computing (IComp)Federal University of Amazon (UFAM)
Introduction - Environmental Motivation
The study of environmental conditions allow:
maintain the quality of life, and to preserve the species.
The loss of species is an irreversible process!The loss of species is an irreversible process!
The variation of species populations enables to:
identify environmental problems in the early stages, and
establish strategies for the conservation of biological diversity.
Introduction - Environmental Motivation
Variations in amphibian populations are related to pollution, deforestation, urbanization, etc.
Frogs can be used as indicators for detecting environmental stress.
Figure: Percentage of threatened species in the red list. Figure adapted from [Stuart et al., 2004].
Introduction – Objectives
Classify frog species of tropical forests based on the vocalizations
using wireless sensor networks and machine learning technique.*
4* Consideration: Restrictions on the hardware.
Introduction - Challenges
Develop a method that does not need human intervention.
Characterize the spectral frequency of frog.
Extract and select the optimal set of features.
Define the classification technique.
Get the minimum set of features using genetic algorithm.
Obtain the cost of processing characteristics.
Correlate the processing cost and success rate.
Maximize the benefit cost rate.
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WSN and Machine Learning
Related Work
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Author Animal Features Classifier Results WSN
Taylor et al. [1996] Bufo marinus Spectrograma C4.5 60% No
Hu et al. [2005] Bufo marinus Spectrograma C4.5 60% Yes
Yen & Fu [2002]* 4 frog WaveletFisher’s
MLP 71% No
Clemins [2005] elephant MFCCsPLP
HMMDTW
69%73%
No
Cai et al. [2007] 14 bird MFCCs ANN 81% - 86% Yes
Huang et al. [2009]* 5 frog S - B - ZC k-NNSVM
83% - 100%82% - 100%
No
Vaca-Castaño & Rodriguez [2010]*
10 bird20 frog
MFCCsPCA
k-NN 86%91%
Yes
Han et al. [2011]* 9 frog S - Hs - Hr k-NN 83% - 100% No
* Work implemented and used in the comparisons.
Our approach
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Figure: Parametrization of vocalizations.
Figure: Anuran classification stages. Figure: Pre-processing steps.
Features
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Figure: Mel-Fourier Cepstral Coefficients (MFCCs).
Figure: Wavelet Transform with Lifting Scheme.
Obtain the features
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Figure: Feature extraction.
Spectrogram
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Figure: Audio sample (wave form and spectrogram) for the Adenomera andreae..
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Features
Feature Complexity order Computational cost
Pitch O(L) 3L − 1
B O(Nlog(N)) 2M + 2M + Nlog(N)
12 MFCC’s O(Nlog(N)) Nlog(N) + N + mR
S O(Nlog(N)) 2M + Nlog(N)
H1 O(L) L + i
H2 O(L) L + i
ZC O(L) L
E O(L) L
Pw O(L) L
Comparison between MFCCs and Wavelet
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Features k-NN
0.4 0.5 0.6
Wavelet FeaturesDaubechies Transform
96.35%(3) 97.86%(1) 98.22%(1)
Wavelet FeaturesHaar Transform
96.70%(1) 97.90%(1) 98.38%(1)
MFCCs 99.19%(9) 99.36%(2) 99.19%(1)
Table: Success rate in relation to alpha, using cross-validation fold = 10.
Applying the Wilcoxon test, with 95% significance level (α = 0.5), we conclude that the MFCCs have better performance.
Comparison between MFCCs and Wavelet
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Objective: To determine the optimal subset of features by applying GA.
Comparison between MFCCs and Wavelet
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Features Classificationbefore GA
Crossover 50%Mutation 40%
Success rate Crossover 60%Mutation 20%
Success rate
9 features with Db
97.86%(1) 1,2,3,5 93.73% 1,2,3,4,5,6,8,9 96.83%
9 featureswith Haar
97.90%(1)* 2,3,4,5,6,8,9 96.47% 1,2,3,4,5,6,7,8,9 97.90%*
12 MFCCs 99.36%(2)* 1,2,3,4,5,6,7,11 99.08% 1,2,3,4,5,6,7,8,911,12
99.33%*
Case of Study
fs = 44.1kHz
fs =5.5kHz
fs = 11kHz
Conclusions
We indicated how best set of features to choose the 12 MFCCs.
You can optimize costs by using 8 MFCCs, although the method loses generality.
The MFFCs have:
✔ Better success rate;✔ Constant cost, regardless of hardware, and✔ Immunity to environmental and quantization noise.
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Questions?
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Thanks