Acoustic Identification of Nocturnal Bird Species

  • Michelangelo Acconcjaioco
  • Stavros NtalampirasEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1209)


Automatic classification of bird species based on their vocalizations is a topic of crucial relevance for the research conducted by biologists, ornithologists, ecologists, and related disciplines. This work in concentrated on nocturnal species; even though the analysis of their population trends is a key indicator, there is a gap in the literature addressing their audio-based identification. After compiling a suitable dataset including six nocturnal bird species, this study employs both supervised (k-Nearest Neighbors, Support Vector Machines) and unsupervised (k-means) methods operating in the feature space formed by Mel-spectrograms. We conclude that automatic classification based on k-Nearest Neighbour and Support Vector Machines provide almost excellent results with a recognition rate in the order of 90%.


Bird vocalizations Bioacoustics Acoustic ecology Machine learning Audio signal processing Audio pattern recognition 


  1. 1.
    Bastas, S.A.: Nocturnal bird call recognition system for wind farm applications. Master’s thesis, The University of Toledo (2011)Google Scholar
  2. 2.
    Fagerlund, S., Laine, U.K.: New parametric representations of bird sounds for automatic classification. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8247–8251, May 2014.
  3. 3.
    Farnsworth, A., et al.: Reconstructing velocities of migrating birds from weather radar - a case study in computational sustainability. AI Mag. 35(2), 31 (2014). Scholar
  4. 4.
    Fink, D., et al.: Crowdsourcing meets ecology: hemisphere-wide spatiotemporal species distribution models. AI Mag. 35(2), 19 (2014). Scholar
  5. 5.
    Giannakopoulos, T., Pikrakis, A.: Introduction to Audio Analysis: A MATLAB® Approach. Academic Press, Cambridge (2014)Google Scholar
  6. 6.
    Koops, H.V., van Balen, J., Wiering, F.: Automatic segmentation and deep learning of bird sounds. In: Mothe, J., et al. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 261–267. Springer, Cham (2015). Scholar
  7. 7.
    Laiolo, P.: The emerging significance of bioacoustics in animal species conservation. Biol. Conserv. 143(7), 1635–1645 (2010). Scholar
  8. 8.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008). Scholar
  9. 9.
    Ntalampiras, L.A., et al.: Automatic classification of cat vocalizations emitted in different contexts. Animals 9(8), 543 (2019). Scholar
  10. 10.
    Ntalampiras, S.: Hybrid framework for categorising sounds of Mysticete whales. IET Signal Proc. 11(4), 349–355 (2017). Scholar
  11. 11.
    Ntalampiras, S.: Moving vehicle classification using wireless acoustic sensor networks. IEEE Trans. Emerg. Top. Comput. Intell. 2(2), 129–138 (2018). Scholar
  12. 12.
    Ntalampiras, S.: Audio pattern recognition of baby crying sound events. J. Audio Eng. Soc. 63(5), 358–369 (2015). Scholar
  13. 13.
    Ntalampiras, S.: Bird species identification via transfer learning from music genres. Ecol. Inform. (2018).,
  14. 14.
    Ntalampiras, S.: Automatic acoustic classification of insect species based on directed acyclic graphs. J. Acoust. Soc. Am. 145(6), EL541–EL546 (2019). Scholar
  15. 15.
    Ntalampiras, S.: Generalized sound recognition in reverberant environments. J. Audio Eng. Soc. 67(10), 772–781 (2019). Scholar
  16. 16.
    Ntalampiras, S.: On acoustic monitoring of farm environments. In: Thampi, S.M., Marques, O., Krishnan, S., Li, K.-C., Ciuonzo, D., Kolekar, M.H. (eds.) SIRS 2018. CCIS, vol. 968, pp. 53–63. Springer, Singapore (2019). Scholar
  17. 17.
    Ntalampiras, S., Potamitis, I., Fakotakis, N.: A multidomain approach for automatic home environmental sound classification, Makuhari, Japan, September (2010)Google Scholar
  18. 18.
    Potamitis, I.: Automatic classification of a taxon-rich community recorded in the wild. PLoS ONE 9(5), e96936 (2014). Scholar
  19. 19.
    Raghuram, M.A., Chavan, N.R., Belur, R., Koolagudi, S.G.: Bird classification based on their sound patterns. Int. J. Speech Technol. 19(4), 791–804 (2016). Scholar
  20. 20.
    Rai, P., Golchha, V., Srivastava, A., Vyas, G., Mishra, S.: An automatic classification of bird species using audio feature extraction and support vector machines. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 1, pp. 1–5, August 2016.
  21. 21.
    Riede, K.: Monitoring biodiversity: analysis of Amazonian rainforest sounds. Ambio 22(8), 546–548 (1993). Scholar
  22. 22.
    Riede, K.: Acoustic monitoring of Orthoptera and its potential for conservation. J. Insect Conserv. 2(3/4), 217–223 (1998). Scholar
  23. 23.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). Scholar
  24. 24.
    Salamon, J., et al.: Towards the automatic classification of avian flight calls for bioacoustic monitoring. PLOS ONE 11(11), e0166866 (2016). Scholar
  25. 25.
    Stowell, D., Benetos, E., Gill, L.F.: On-bird sound recordings: automatic acoustic recognition of activities and contexts. IEEE/ACM Trans. Audio Speech Lang. Process. 25(6), 1193–1206 (2017). Scholar
  26. 26.
    Stowell, D., Wood, M.D., Pamuła, H., Stylianou, Y., Glotin, H.: Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge. Methods Ecol. Evol. 10(3), 368–380 (2018). Scholar
  27. 27.
    Witten, I.H., Frank, E., Hall, M.A.: Data mining: practical machine learning tools and techniques. In: Morgan Kaufmann Series in Data Management Systems, 3 edn. Morgan Kaufmann, Amsterdam (2011).

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Michelangelo Acconcjaioco
    • 1
  • Stavros Ntalampiras
    • 1
    Email author
  1. 1.Department of Computer ScienceUniversity of MilanMilanItaly

Personalised recommendations