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Neural networks that locate and identify birds through their songs

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Abstract

In this work, we present a set of algorithms that allow the location and identification of birds through their songs. To achieve the first objective, neural networks capable of reconstructing the position of the subject are trained from a set of differences in the arrival times of a sound signal to the different microphones in an array. For the second objective, a dynamical system is used to generate surrogate songs, similar to those of a given set of subjects, to train a neural network so that it can classify subjects. Taken together, they constitute an interesting tool for the automatic monitoring of small bird populations.

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Data availability statement

This manuscript has associated data in a data repository. [Authors’ comment: All data included in this manuscript is available upon request to the corresponding author.]

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Authors and Affiliations

  1. Departamento de Física, FCEyN, Universidad de Buenos Aires, Buenos Aires, Argentina

    Roberto A. Bistel & Gabriel B. Mindlin

  2. IFIBA, CONICET, Buenos Aires, Argentina

    Roberto A. Bistel, Alejandro Martinez & Gabriel B. Mindlin

Authors
  1. Roberto A. Bistel
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  2. Alejandro Martinez
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  3. Gabriel B. Mindlin
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Correspondence to Gabriel B. Mindlin.

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Bistel, R.A., Martinez, A. & Mindlin, G.B. Neural networks that locate and identify birds through their songs. Eur. Phys. J. Spec. Top. 231, 185–194 (2022). https://doi.org/10.1140/epjs/s11734-021-00405-5

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  • DOI: https://doi.org/10.1140/epjs/s11734-021-00405-5

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