Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)

Bioacoustics Research Program 2014. Raven Pro: Interactive Sound Analysis Software (Version 1.5) [Computer software]. – Ithaca, NY: The Cornell Lab of Ornithology Available from http://www.birds.cornell.edu/raven.Search in Google Scholar

Borker, A. L., Halbert, P., McKown, M. W., Tershy, B. R. & Croll, D. A. 2015. A comparison of automated and traditional monitoring techniques for marbled murrelets using passive acoustic sensors. – Wildlife Society Bulletin 39: 813–818. DOI: 10.1002/wsb.60810.1002/wsb.608Open DOISearch in Google Scholar

Catchpole, C. K., Slater, P. J. B. 2008. Bird song: biological themes and variations, 2^nd ed. – Cambridge University Press, Cambridge10.1017/CBO9780511754791Search in Google Scholar

Garamszegi, L. Zs., Eens, M. & Török, J. 2008. Birds Reveal their Personality when Singing. – PLoS One 3(7). DOI: 10.1371/journal.pone.000264710.1371/journal.pone.0002647244145418612388Open DOISearch in Google Scholar

Garamszegi, L. Zs., Török, J., Hegyi, G., Szöllõsi, E., Rosivall, B. & Eens, M. 2007. Age-dependent expression of song in the Collared Flycatcher, Ficedula albicollis. – Ethology 113: 246–256. DOI: 10.1111/j.1439-0310.2007.01337.x10.1111/j.1439-0310.2007.01337.xOpen DOISearch in Google Scholar

Garamszegi, L. Zs., Zagalska-Neubauer, M., Canal, D., Blazi, Gy., Laczi, M., Nagy, G., Szőllősi, E., Vaskuti, É. Török, J. & Zsebők, S. 2018. MHC-mediated sexual selection on birdsong: Generic polymorphism, particular alleles and acoustic signals. – Molecular Ecology 27: 2620–2633. DOI: 10.1111/mec.1470310.1111/mec.1470329693314Open DOISearch in Google Scholar

Garamszegi, L. Zs., Zsebők, S. & Török, J. 2012. The relationship between syllable repertoire similarity and pairing success in a passerine bird species with complex song. – Journal of Theoretical Biology 295: 68–76. DOI: 10.1016/j.jtbi.2011.11.01110.1016/j.jtbi.2011.11.01122123372Open DOISearch in Google Scholar

Haavie, J., Borge, T., Bures, S., Garamszegi, L. Zs., Lampe, H. M., Moreno, J., Qvarnström, A., Török, J. & Sætre, G. P. 2004. Flycatcher song in allopatry and sympatry – Convergence, divergence and reinforcement. – Journal of Evolutionary Biology 17: 227–237. DOI: 10.1111/j.1420-9101.2003.00682.x10.1111/j.1420-9101.2003.00682.x15009256Open DOISearch in Google Scholar

Hafner, S. D. & Katz, J. 2017. {monitoR}: Acoustic template detection in R. Retrieved from http://www.uvm.edu/rsenr/vtcfwru/R/?Page=monitoR/monitoR.htmSearch in Google Scholar

Hopp, S. L., Owren, M. J. & Evans, C. S. 1998. Animal acoustic communication: sound analysis and research methods. – Springer-Verlag Berlin Heidelberg10.1007/978-3-642-76220-8Search in Google Scholar

Lachlan, R. F., Ratmann, O. & Nowicki, S. 2018. Cultural conformity generates extremely stable traditions in bird song. – Nature Communications 9: 2417. DOI: 10.1038/s41467-018-04728-110.1038/s41467-018-04728-1601040929925831Open DOISearch in Google Scholar

Laiolo, P. 2010. The emerging significance of bioacoustics in animal species conservation. – Biological Conservation 143: 1635–1645. DOI: 10.1016/j.biocon.2010.03.02510.1016/j.biocon.2010.03.025Open DOISearch in Google Scholar

Mac Aodha, O., Gibb, R., Barlow, K. E., Browning, E., Firman, M., Freeman, R., Harder, B., Kinsey, L., Mead, G. R., Newson, S. E., Pandourski, I., Parsons, S., Russ, J., Szodoray-Paradi, A., Szodoray-Paradi, F., Tilova, E., Girolami, M., Brostow, G. & Jones, K. E. 2018. Bat detective-Deep learning tools for bat acoustic signal detection. – PLoS Computational Biology 14: 1–19. DOI: 10.1371/journal.pcbi.100599510.1371/journal.pcbi.1005995584316729518076Open DOISearch in Google Scholar

Priyadarshani, N., Marsland, S. & Castro, I. 2018. Automated birdsong recognition in complex acoustic environments: a review. – Journal of Avian Biology 49(5): 1–27. DOI: 10.1111/jav.0144710.1111/jav.01447Open DOISearch in Google Scholar

R Core Team 2018. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria – Available online at https://www.R-project.org/Search in Google Scholar

Rahman, M. A. & Wang, Y. 2016. Optimizing intersection-over-union in deep neural networks for image segmentation. – Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10072 LNCS: 234–244. DOI: 10.1007/978-3-319-50835-1_2210.1007/978-3-319-50835-1_22Open DOISearch in Google Scholar

Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. 2016. You Only Look Once: Unified, Real-Time Object Detection. Retrieved from https://arxiv.org/abs/1506.02640v510.1109/CVPR.2016.91Search in Google Scholar

Redmon, J. & Farhadi, A. 2018. YOLOv3: An Incremental Improvement. – Retrieved from http://arxiv.org/abs/1804.02767Search in Google Scholar

Stowell, D., Petrusková, T., Šálek, M. & Linhart, P. 2018. Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions. – Retrieved from http://arxiv.org/abs/1810.09273Search in Google Scholar

Stowell, D., Wood, M. D., Pamuła, H., Stylianou, Y. & Glotin, H. 2019. Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. – Methods in Ecology and Evolution 10: 368–380. DOI: 10.1111/2041-210X.1310310.1111/2041-210X.13103Search in Google Scholar

Sueur, J., Aubin, T. & Simonis. C. 2008. Seewave, a Free Modular Tool for Sound Analysis and Synthesis. Bio-acoustics The International Journal of Animal Sound and its Recording 18:213–226. DOI: 10.1080/09524622. 2008.975360010.1080/09524622.2008.9753600Open DOISearch in Google Scholar

Tchernichovski, O., Nottebohm, F., Ho, C. E., Pesaran, B. & Mitra, P. P. 2000. A procedure for an automated measurement of song similarity. – Animal Behaviour 59: 1167–1176. DOI: 10.1006/anbe.1999.141610.1006/anbe.1999.141610877896Open DOISearch in Google Scholar

Vellema, M., Diales Rocha, M., Bascones, S., Zsebők, S., Dreier, J., Leitner, S., Van der Linden, A., Brewer, J. & Gahr, M. 2019. Accelerated redevelopment of vocal skills is preceded by lasting reorganization of the song motor circuitry. – Elife 8: 1–46. DOI: 10.7554/elife.4319410.7554/elife.43194657052631099755Open DOISearch in Google Scholar

Zachar, G., Tóth, A. S., Gerecsei, L. I., Zsebők, S., Ádám, Á. & Csillag, A. 2019. Valproate exposure in ovo attenuates the acquisition of social preferences of young post-hatch Domestic Chicks. – Frontiers in Physiology 10: 881. DOI: 10.3389/fphys.2019.0088110.3389/fphys.2019.00881664651731379596Search in Google Scholar

Zsebők, S., Blázi, G., Laczi, M., Nagy, G., Vaskuti, É. & Garamszegi, L. Zs. 2018a “Ficedula”: an open-source MATLAB toolbox for cutting, segmenting and computer-aided clustering of bird song. – Journal of Ornithology 159: 1105–1111. DOI: 10.1007/s10336-018-1581-910.1007/s10336-018-1581-9Search in Google Scholar

Zsebők, S., Herczeg, G., Blázi, G., Laczi, M., Nagy, G., Török, J. & Garamszegi, L. Zs. 2018b Minimum spanning tree as a new, robust repertoire size comparison method: simulation and test on birdsong. – Behavioral Ecology and Sociobiology 72: 48. DOI: 10.1007/s00265-018-2467-910.1007/s00265-018-2467-9Open DOISearch in Google Scholar

Zsebők, S., Herczeg, G., Blázi, G., Laczi, M., Nagy, G., Szász, E., Markó, G., Török, J. & Garamszegi, L. Zs. 2017. Short- and long-term repeatability and pseudo-repeatability of bird song: sensitivity of signals to varying environments. – Behavioral Ecology and Sociobiology 71: 154. DOI: 10.1007/s00265-017-2379-010.1007/s00265-017-2379-0Open DOISearch in Google Scholar