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Insights from Deep Learning in Feature Extraction for Non-supervised Multi-species Identification in Soundscapes

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Advances in Artificial Intelligence – IBERAMIA 2022 (IBERAMIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13788))

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Abstract

Biodiversity monitoring has taken a relevant role in conservation management plans, where several methodologies have been proposed to assess biological information of landscapes. Recently, soundscape studies have allowed biodiversity monitoring by compiling all the acoustic activity present in landscapes in audio recordings. Automatic species detection methods have shown to be a practical tool for biodiversity monitoring, providing insight into the acoustic behavior of species. Generally, the proposed methodologies for species identification have four main stages: signal pre-processing, segmentation, feature extraction, and classification. Most proposals use supervised methods for species identification and only perform for a single taxon. In species identification applications, performance depends on extracting representative species features. We present a feature extraction analysis for multi-species identification in soundscapes using unsupervised learning methods. Linear frequency cepstral coefficients (LFCC), variational autoencoders (VAE), and the KiwiNet architecture, which is a convolutional neural network (CNN) based on VGG19, were evaluated as feature extractors. LFCC is a frequency-based method, while VAE and KiwiNet belong to the deep learning area. In ecoacoustic applications, frequency-based methods are the most widely used. Finally, features were tested by a clustering algorithm that allows species recognition from different taxa. The unsupervised approaches performed multi-species identification between 78%–95%.

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Acknowledgments

This work was supported by Universidad de Antioquia, Instituto Tecnológico Metropolitano de Medellín, Alexander von Humboldt Institute for Research on Biological Resources and Colombian National Fund for Science, Technology and Innovation, Francisco Jose de Caldas - MINCIENCIAS (Colombia) [Program No. 111585269779].

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Authors and Affiliations

  1. SISTEMIC, Facultad de Ingeniería, Universidad de Antioquia, Calle 70 No. 52-21, Medellín, Colombia

    Maria J. Guerrero, Jonathan Restrepo & Claudia Isaza

  2. MIRP, Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Calle 54a No 30-99, Medellín, Colombia

    Daniel A. Nieto-Mora

  3. Instituto de Biología, Grupo Herpetológico de Antioquia, Universidad de Antioquia, Calle 70 No. 52-21, Medellín, Colombia

    Juan M. Daza

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  1. Maria J. Guerrero
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  2. Jonathan Restrepo
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  3. Daniel A. Nieto-Mora
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  4. Juan M. Daza
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  5. Claudia Isaza
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Correspondence to Maria J. Guerrero .

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Editors and Affiliations

  1. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Ana Cristina Bicharra Garcia

  2. Fluminense Federal University, Niterói, Brazil

    Mariza Ferro

  3. University of Cartagena, Cartagena de Indias, Colombia

    Julio Cesar Rodríguez Ribón

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Guerrero, M.J., Restrepo, J., Nieto-Mora, D.A., Daza, J.M., Isaza, C. (2022). Insights from Deep Learning in Feature Extraction for Non-supervised Multi-species Identification in Soundscapes. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_19

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  • DOI: https://doi.org/10.1007/978-3-031-22419-5_19

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