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- W2030638838 abstract "A sparse representation classifier (SRC) has been adapted and applied to spectrograms to identify bottlenose dolphin whistles by their types. The classifier that relies on near completeness of the training features renders their choice no longer crucial as long as criteria are met to assure signal sparsity. Signal sparsity is ensured via the employment of a robust, effective, and computationally simple local binary patterns (LBP) operator that eliminates the need for costly denoising and contour tracking operations. The performance of the proposed method is compared to classifier-feature combinations of the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers, and feature vectors of time-frequency contour parameters, Fourier descriptors, and raw data. The experimental results demonstrate superior accuracy and robustness of the proposed method to classify dolphin whistles into distinct call types. The method can be generalized to all narrowband signals with time varying spectra." @default.
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- W2030638838 date "2013-11-01" @default.
- W2030638838 modified "2023-10-17" @default.
- W2030638838 title "Sparse representation classification of dolphin whistles using local binary patterns" @default.
- W2030638838 doi "https://doi.org/10.1121/1.4830536" @default.
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