Abstract—Automatic animal sound classification and retrieval is very helpful for bioacoustic and audio retrieval applications. In this paper we propose a system to define and extract a set of acoustic features from all archived wild animal sound recordings that is used in subsequent feature selection, classification and retrieval tasks. The database consisted of sounds of six wild animals. The Fractal Dimension analysis based segmentation was selected due to its ability to select the right portion of signal for extracting the features. The feature vectors of the proposed algorithm consist of spectral, temporal and perceptual features of the animal vocalizations. The minimal Redundancy, Maximal Relevance (mRMR) feature selection analysis was exploited to increase the classification accuracy at a compact set of features. These features were used as the inputs of two neural networks, the k-Nearest Neighbor (kNN), the Multi-Layer Perceptron (MLP) and its fusion. The proposed system provides quite robust approach for classification and retrieval purposes, especially for the wild animal sounds.
Index Terms—FD - Fractal Dimension, KNN - k-Nearest Neighbor classifier, MLP - Multilayer Perceptron, mRMR - minimal Redundancy - Maximal Relevance.
S.Gunasekaran is doing research with Department of Computer Science, University of Kerela, Trivandrum, India. phone: +91-90361-96856; e-mail: yesgunaa@ gmail.com
Dr. K.Revathy is with Department of Computer Science, University of Kerela, Trivandrum, India as Professor (Retd). e-mail: revathy_srp@yahoo.com
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Cite: S.Gunasekaran and K.Revathy, "Automatic Recognition and Retrieval of Wild Animal Vocalizations,"
International Journal of Computer Theory and Engineering vol. 3, no. 1, pp. 136-140, 2011.
