Feature and Dissimilarity Representations for the Sound-Based Recognition of Bird Species

  • José Francisco Ruiz-Muñoz
  • Mauricio Orozco-Alzate
  • César Germán Castellanos-Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


Pattern recognition and digital signal processing techniques allow the design of automated systems for avian monitoring. They are a non-intrusive and cost-effective way to perform surveys of bird populations and assessments of biological diversity. In this study, a number of representation approaches for bird sounds are compared; namely, feature and dissimilarity representations. In order to take into account the non-stationary nature of the audio signals and to build robust dissimilarity representations, the application of the Earth Mover’s Distance (EMD) to time-varying measurements is proposed. Measures of the leave-one-out 1-NN performance are used as comparison criteria. Results show that, overall, the Mel-ceptrum coefficients are the best alternative; specially when computed by frames and used in combination with EMD to generate dissimilarity representations.


Automated avian monitoring bird sounds dissimilarity representations feature representations 


  1. 1.
    Brenowitz, E., Margoliash, D., Nordeen, K.: An introduction to birdsong and the avian song system. Journal of Neurobiology 33(5), 495–500 (1997)CrossRefGoogle Scholar
  2. 2.
    Acevedo, M.A., Corrada-Bravo, C.J., Corrada-Bravo, H., Villanueva-Rivera, L.J., Aide, T.M.: Automated classification of bird and amphibian calls using machine learning: A comparison of methods. Ecological Informatics 4(4), 206–214 (2009)CrossRefGoogle Scholar
  3. 3.
    Fagerlund, S.: Bird species recognition using support vector machines. EURASIP Journal on Advances in Signal Processing 2007(1), 64–64 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chou, C., Liu, P., Cai, B.: On the Studies of Syllable Segmentation and Improving MFCCs for Automatic Birdsong Recognition. In: Asia-Pacific Services Computing Conference, APSCC 2008, pp. 745–750. IEEE (2009)Google Scholar
  5. 5.
    Pękalska, E., Duin, R.P.W., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189–208 (2006)CrossRefzbMATHGoogle Scholar
  6. 6.
    Logan, B., Salomon, A.: A music similarity function based on signal analysis. In: IEEE International Conference on Multimedia and Expo, ICME 2001, pp. 745–748 (August 2001)Google Scholar
  7. 7.
    Rubner, Y., Tomasi, C., Guibas, L.: The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Francisco Ruiz-Muñoz
    • 1
  • Mauricio Orozco-Alzate
    • 1
    • 2
  • César Germán Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Departamento de Informática y ComputaciónUniversidad Nacional de ColombiaManizalesColombia

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