Juan G. ColonnaAfonso D. RibasEduardo F. NakamuraEulanda M. dos Santos                 Feature Subset Selection for       ...
Introduction - Environmental MotivationThe study of environmental conditions allow: maintain the quality of life, and to...
Introduction - Environmental Motivation  Variations in amphibian populations are related to pollution,   deforestation, u...
Introduction – Objectives   Classify frog species of tropical forests based on the vocalizations   using wireless sensor n...
Introduction - Challenges  Develop a method that does not need human intervention.  Characterize the spectral frequency ...
Related Work        Author              Animal         Features     Classifier        Results     WSN   Taylor et al. [199...
Our approach  Figure: Anuran classification stages.       Figure: Pre-processing steps.                                   ...
FeaturesFigure: Mel-Fourier Cepstral Coefficients   Figure: Wavelet Transform with Lifting Scheme.(MFCCs).                ...
Obtain the features                 Figure: Feature extraction.                                               9
Spectrogram         Figure: Audio sample (wave form and spectrogram) for         the Adenomera andreae..                  ...
Features            Feature    Complexity order   Computational cost             Pitch          O(L)                3L − 1...
Comparison between MFCCs and Wavelet                Features                                         k-NN                 ...
Comparison between MFCCs and Wavelet  Objective: To determine the optimal subset of features by applying GA.              ...
Comparison between MFCCs and Wavelet   Features    Classification   Crossover 50%      Success rate    Crossover 60%      ...
Case of Study        fs = 44.1kHz                fs = 11kHz                       fs =5.5kHz
ConclusionsWe indicated how best set of features to choose the 12 MFCCs.You can optimize costs by using 8 MFCCs, although ...
Questions?             Thanks                      17
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Feature Subset Selection for Automatically Classifying Anuran Calls Using Sensor Networks

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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).

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Feature Subset Selection for Automatically Classifying Anuran Calls Using Sensor Networks

  1. 1. Juan G. ColonnaAfonso D. RibasEduardo F. NakamuraEulanda M. dos Santos Feature Subset Selection for Automatically Classifying Anuran Calls Using Sensor NetworksInstitute of Computing (IComp)Federal University of Amazon (UFAM)
  2. 2. Introduction - Environmental MotivationThe study of environmental conditions allow: maintain the quality of life, and to preserve the species.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.
  3. 3. 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].
  4. 4. Introduction – Objectives Classify frog species of tropical forests based on the vocalizations using wireless sensor networks and machine learning technique. * * Consideration: Restrictions on the hardware. 4
  5. 5. 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. WSN and  Correlate the processing cost and success rate. Machine Learning  Maximize the benefit cost rate. 5
  6. 6. Related Work 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 Wavelet MLP 71% No Fisher’s Clemins [2005] elephant MFCCs HMM 69% No PLP DTW 73% Cai et al. [2007] 14 bird MFCCs ANN 81% - 86% Yes Huang et al. [2009]* 5 frog S - B - ZC k-NN 83% - 100% No SVM 82% - 100% Vaca-Castaño & 10 bird MFCCs k-NN 86% Yes Rodriguez [2010]* 20 frog PCA 91% Han et al. [2011]* 9 frog S - Hs - Hr k-NN 83% - 100% No * Work implemented and used in the comparisons. 6
  7. 7. Our approach Figure: Anuran classification stages. Figure: Pre-processing steps. Figure: Parametrization of vocalizations. 7
  8. 8. FeaturesFigure: Mel-Fourier Cepstral Coefficients Figure: Wavelet Transform with Lifting Scheme.(MFCCs). 8
  9. 9. Obtain the features Figure: Feature extraction. 9
  10. 10. Spectrogram Figure: Audio sample (wave form and spectrogram) for the Adenomera andreae.. 10
  11. 11. 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 11
  12. 12. Comparison between MFCCs and Wavelet Features k-NN 0.4 0.5 0.6 Wavelet Features 96.35%(3) 97.86%(1) 98.22%(1) Daubechies Transform Wavelet Features 96.70%(1) 97.90%(1) 98.38%(1) Haar Transform 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 theMFCCs have better performance. 12
  13. 13. Comparison between MFCCs and Wavelet Objective: To determine the optimal subset of features by applying GA. 13
  14. 14. Comparison between MFCCs and Wavelet Features Classification Crossover 50% Success rate Crossover 60% Success rate before GA Mutation 40% Mutation 20% 9 features 97.86%(1) 1,2,3,5 93.73% 1,2,3,4,5,6,8,9 96.83% with Db 9 features 97.90%(1)* 2,3,4,5,6,8,9 96.47% 1,2,3,4,5,6,7,8,9 97.90%* with Haar 12 MFCCs 99.36%(2)* 1,2,3,4,5,6,7,11 99.08% 1,2,3,4,5,6,7,8,91 99.33%* 1,12 14
  15. 15. Case of Study fs = 44.1kHz fs = 11kHz fs =5.5kHz
  16. 16. ConclusionsWe indicated how best set of features to choose the 12 MFCCs.You can optimize costs by using 8 MFCCs, although the method losesgenerality.The MFFCs have:✔ Better success rate;✔ Constant cost, regardless of hardware, and✔ Immunity to environmental and quantization noise. 16
  17. 17. Questions? Thanks 17

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