Frog Identification System Based on Local Means K-Nearest Neighbors with Fuzzy Distance Weighting

  • Haryati Jaafar
  • Dzati Athiar Ramli
  • Bakhtiar Affendi Rosdi
  • Shahriza Shahrudin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)


Frog 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. Yet, the k-nearest neighbor (kNN) is one of the popular classifiers and has been applied in various applications. This paper proposes an improvement of kNN in order to evaluate the accuracy of frog sound identification. The recorded sounds of 12 frog species obtained in Malaysia forest have been segmented using short time energy and short time average zero crossing rate while the features are extracted by mel frequency cepstrum coefficient. Finally, a proposed classifier based on local means kNN and fuzzy distance weighting have been employed to identify the frog species. Comparison of the system performances based on kNN, local means kNN and the proposed classifier i.e. fuzzy kNN with manual segmentation and automatic segmentation is evaluated. The results show the proposed classifier outperforms the baseline classifier with accuracy of 94.67 % and 98.33 % for manual and automatic segmentation, respectively.


Frog identification kNN Local means KNN Fuzzy kNN Distance weighting 



The authors would like to thank the financial support provided by Universiti Sains Malaysia Short Term Grant, 304/PELECT/60311048, Research University Grant 814161 and Research University Grant 814098 for this project.


  1. 1.
    Bevier CR, Sonnevend A, Kolodziejek J, Nowotny N, Nielsen PF, Conlon JM (2004) Purification and characterization of antimicrobial peptides from the skin secretions of the mink frog Rana septentrionalis. Comp Biochem Physiol 139(1–3):31–38Google Scholar
  2. 2.
    Obrist MK, Pavan G, Sueur J, Riede K, Llusia D, Márquez R (2010) Bioacoustic approaches in biodiversity inventories. In: Manual on field recording techniques and protocols for all taxa biodiversity inventories. Abc taxa, vol 8. pp 68–99Google Scholar
  3. 3.
    Huang CJ, Yang YJ, Yang DX, Chen YJ (2009) Frog classification using machine learning techniques. Expert Syst Appl 36:3737–3743CrossRefGoogle Scholar
  4. 4.
    Han NC, Muniandy SV, Dayou J (2011) Acoustic classification of Australian anurans based on hybrid spectral-entropy approach. J Appl Acoust 72:639–645CrossRefGoogle Scholar
  5. 5.
    Mitani Y, Hamamoto Y (2006) A local mean-based nonparametric classifier. Pattern Recogn Lett 27:1151–1159CrossRefGoogle Scholar
  6. 6.
    Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic, LondonGoogle Scholar
  7. 7.
    Zeng Y, Yang Y, Zhao L (2009) Nonparametric classification based on local mean and class statistics. Expert Syst Appl 36:8443–8448CrossRefGoogle Scholar
  8. 8.
    Gou J, Yi Z, Du L, Xiong T (2012) A local mean-based k-nearest centroid neighbor classifier. Comput J 55(9):1058–1071CrossRefGoogle Scholar
  9. 9.
    Zuo W, Wang K, Zhang H, Zhang D (2007) Kernel difference-weighted k-nearest neighbors classification. ICIC 2:861–870Google Scholar
  10. 10.
    Jena PK, Chattopadhya S (2012) Comparative study of fuzzy k-nearest neighbor and fuzzy c-means algorithms. Int J Comput Appl 57(7):22–32Google Scholar
  11. 11.
    Jaafar H, Ramli DA (2013) Automatic syllables segmentation for frog identification system. In: 2013 IEEE international colloquium on signal processing and its application, vol 9Google Scholar
  12. 12.
    Hasan MH, Jaafar H, Ramli DA (2012) Evaluation on score reliability for biometric speaker authentication system. J Comput Sci 8(9):1554–1563CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Haryati Jaafar
    • 1
  • Dzati Athiar Ramli
    • 1
  • Bakhtiar Affendi Rosdi
    • 1
  • Shahriza Shahrudin
    • 2
  1. 1.School of Electrical and Electronic EngineeringUSM Engineering CampusNibong TebalMalaysia
  2. 2.School of Pharmacy SciencesUSMMindenMalaysia

Personalised recommendations