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A three-parameter model for classifying anurans into four genera based on advertisement calls
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10.1121/1.4768878
/content/asa/journal/jasa/133/1/10.1121/1.4768878
http://aip.metastore.ingenta.com/content/asa/journal/jasa/133/1/10.1121/1.4768878

Figures

Image of FIG. 1.
FIG. 1.

Typical spectrograms of anuran vocalizations from the Bufo, Hyla, Leptodactylus, and Rana genera. (a) Bufo americanus, (b) Bufo bufo, (c) Bufo japonicas, (d) Hyla chrysoscelis, (e) Hyla japonica, (f) Hyla minuta, (g) Leptodactylus bufonius, (h) Leptodactylus fuscus, (i) Leptodactylus pentadactylus, (j) Rana arvalis, (k) Rana boylii, (l) Rana tagoi. Dynamic range: 55 dB.

Image of FIG. 2.
FIG. 2.

Mean values for the DF, coefficient of variation of the root mean square of the amplitude CVA, and SF for the 142 species of anurans included in the sample.

Tables

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TABLE I.

Classification accuracy of the multinomial logistic regression and SVM models. [Note: TP: true positives. FP: false positives. Percentages of true positives and false positives represent the means and standard error of the mean of 100 iterations of stratified 10-fold cross validation. The baseline accuracy rate was 25% (uniform prior distribution). The multinomial logistic regression was conducted using the mean values of DF, CVA, and SF for each recording, whereas the SVM algorithm used the normalized mean values of DF, CVA, and SF.]

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TABLE II.

Classification accuracy of K-nn, MGD, and GMM used on DF, CVA, and SF. [Note: TP: true positives. FP: false positives. Percentages of true positives and false positives represent the means and standard error of the mean of 100 iterations of stratified 10-fold cross validation. The baseline accuracy rate was 28.2% (the majority class, Hyla). The K-nn algorithm used the Euclidean distances of the normalized mean values of DF, CVA, and SF. The Gaussian mixture for the GMM model used three components.]

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TABLE III.

Classification accuracy of MGD and GMM used on MFCCs. [Note: TP: true positives. FP: false positives. Percentages of true positives and false positives represent the means and standard error of the mean of 100 iterations of stratified 10-fold cross validation. The baseline accuracy rate was 28.2% (the majority class, Hyla). The Gaussian mixture for the GMM model used nine components.]

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TABLE IV.

Confusion matrix obtained with the K-nn algorithm. (Note: Results are for 100 iterations of stratified 10-fold cross validation.)

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TABLE V.

Classification accuracy on 52 out-of-sample recordings. (Note. Three parameters: low-level acoustic parameters identified by forward-stepwise multinomial logistic regression. TP: true positives. Nn-ranking: median nearest-neighbor ranking of in-sample recordings for the species corresponding to the out-of-sample recordings, with the percentage of rankings in the ten nearest neighbors in parentheses. ***: p < 0.001, **: p < 0.01, * p < 0.05, n.s: not significant.)

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/content/asa/journal/jasa/133/1/10.1121/1.4768878
2013-01-03
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A three-parameter model for classifying anurans into four genera based on advertisement calls
http://aip.metastore.ingenta.com/content/asa/journal/jasa/133/1/10.1121/1.4768878
10.1121/1.4768878
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