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Table 4 WASN-based animal recognition systems

From: Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

  Approach Features Number of features Classifier Dataset (no. of classes) = no of files Recognition accuracy (Recall) Sampling frequency
1 Croker et al. [11] Frequency and time 5 ED Frog (5) = 100 Recall = 85%
Accuracy = 89%
16 kHz
2 Dang et al. [12] Envelope Extraction Not specified Matched filtering Frog (3) = not specified Accuracy = 90% < 10 kHz
3 Wei et al. [13] From Gradient Projection for Sparse Reconstruction featureless using a sparse representation Their own \({\varvec{\iota}}\)1-minimization Sparse Approximation-based classifier Frog (14) = 228 Recall \(\approx\) 98% 24 kHz
crickets (20) = 663 Recall \(\approx\) 50%
4 Colonna et al. [10] Wavelet 4 k-NN Anurans(9) = 49 syllables 96.25%
94.16
86.96%
44.1 kHz
11 kHz
5.5 kHz
5 Algobail et al. [19] Time 2 ED Animals (7) = 114 81.34% 44.1 kHz
6 Our scheme Wavelet 2 MD
ED
Animals (12) = 587 85.59%
86.06%
8 kHz