Frog sound identification using extended k-nearest neighbor classifier

• Main Authors: Jaafar, H. (Author), Muhammad N. (Author), Mukahar, N. (Author), Ramli, D.A (Author), Rosdi, B.A (Author)
• Format: Article
• Language: English
• Published: Institute of Physics Publishing 2017
• Subjects: Classification performance; Environmental Monitoring; Fuzzy k nearest neighbor (FKNN); K nearest neighbor (KNN); K-nearest neighbor classifier; Mel frequency cepstrum coefficients; Motion compensation; Mutual k-nearest neighbors; Nearest neighbor search; Sound identification; Speech recognition
• Online Access: View Fulltext in Publisher; View in Scopus

 Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases. © Published under licence by IOP Publishing Ltd.