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Automatic Identification of Bird Species from Audio

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Intelligent Information and Database Systems (ACIIDS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

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Abstract

Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which makes their real time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains.

The goal of this work is to propose a methodology able to identify bird species by its chirp. In this paper we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks and Recurrent Neural Networks to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

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Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.

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Authors and Affiliations

  1. Instituto Superior de Engenharia do Porto, Rua Dr. Bernardino de, Almeida, 431, 4200-072, Porto, Portugal

    Silvestre Carvalho & Elsa Ferreira Gomes

  2. INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Elsa Ferreira Gomes

Authors
  1. Silvestre Carvalho
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  2. Elsa Ferreira Gomes
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Corresponding author

Correspondence to Elsa Ferreira Gomes .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Carvalho, S., Gomes, E.F. (2021). Automatic Identification of Bird Species from Audio. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_4

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73279-0

  • Online ISBN: 978-3-030-73280-6

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