Skip to main content

Towards Automatic Large-Scale Identification of Birds in Audio Recordings

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9283)


This paper presents a computer-based technique for bird species identification at large scale. It automatically identifies multiple species simultaneously in a large number of audio recordings and provides the basis for the best scoring submission to the LifeCLEF 2014 Bird Identification Task. The method achieves a Mean Average Precision of 51.1% on the test set and 53.9% on the training set with an Area Under the Curve of 91.5% during cross-validation. Besides a general description of the underlying classification approach a number of additional research questions are addressed regarding the choice of features, selection of classifier hyperparameters and method of classification.


  • Bird Identification
  • Information retrieval
  • Biodiversity
  • Spectrogram segmentation
  • Median Clipping
  • Template matching
  • Decision trees

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Frommolt, K.-H., Bardeli, R., Clausen, M. (eds.) Computational bioacoustics for assessing biodiversity. Proc. of the int. expert meeting on IT-based detection of bioacoustical patterns (2008)

    Google Scholar 

  2. Bardeli, R., Wolff, D., Kurth, F., Koch, M., Tauchert, K.-H., Frommolt, K.-H.: Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letter 31(23), 1524–1534 (2009)

    Google Scholar 

  3. Briggs, F., Lakshminarayanan, B., Neal, L., et al.: Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach. The Journal of the Acoustical Society of America 131(6), 4640–4650 (2012). doi:10.1121/1.4707424

    CrossRef  Google Scholar 

  4. Potamitis, I.: Automatic Classification of Taxon-Rich Community Recorded in the Wild. PLoS ONE 9(5), e96936 (2014). doi:10.1371/journal.pone.0096936

    CrossRef  Google Scholar 

  5. Glotin, H., Goëau, H., Vellinga, W-P., Rauber, A.: LifeCLEF bird identification task 2014. In: CLEF working notes (2014)

    Google Scholar 

  6. Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. (, ISSN 1613-0073, (2014).

  7. Eyben, F., Wöllmer, M., Schuller, B.: openSMILE - the munich versatile and fast open-source audio feature extractor. In: Proc. ACM Multimedia (MM), pp. 1459–1462. ACM, Florence, Italy (2010). ISBN 978-1-60558-933-6, doi:10.1145/1873951.1874246

  8. Geiger, J.T., Schuller, B., Rigoll, G.: Large-scale audio feature extraction and svm for acoustic scenes classification. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2013. IEEE (2013)

    Google Scholar 

  9. Lewis, J.P.: Fast Normalized Cross-Correlation. Industrial Light and Magic (1995)

    Google Scholar 

  10. Fodor, G.: The ninth annual MLSP competition: first place. In: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–2 (2013). doi:10.1109/MLSP.2013.6661932

  11. Lasseck, M.: Bird song classification in field recordings: winning solution for NIPS4B 2013 competition. In: Glotin, H. et al. (eds.) Proc. of int. symp. Neural Information Scaled for Bioacoustics,, joint to NIPS, Nevada, pp. 176–181 (2013)

    Google Scholar 

  12. Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. JMLR 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  13. Guyon, I., Weston, J., Barnhill, S., et al.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1–3), 389–422 (2002)

    CrossRef  MATH  Google Scholar 

  14. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 63(1), 3–42 (2006)

    CrossRef  MATH  Google Scholar 

  15. Joly, A., Goëau, H., Bonnet, P. et al.: Are multimedia identification tools biodiversity-friendly? In: Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data (2014). doi:10.1145/2661821.2661826

  16. Adelson, E.H., Anderson, C.H., Bergen, J.R., et al.: Pyramid Method in Image Processing. RCA Engineer 29(6), 33–41 (1984)

    Google Scholar 

  17. Animal Sound Archive Berlin.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Mario Lasseck .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Lasseck, M. (2015). Towards Automatic Large-Scale Identification of Birds in Audio Recordings. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24026-8

  • Online ISBN: 978-3-319-24027-5

  • eBook Packages: Computer ScienceComputer Science (R0)