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Acoustic Identification of Nocturnal Bird Species

  • Michelangelo Acconcjaioco
  • Stavros NtalampirasEmail author
Conference paper
  • 32 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1209)

Abstract

Automatic classification of bird species based on their vocalizations is a topic of crucial relevance for the research conducted by biologists, ornithologists, ecologists, and related disciplines. This work in concentrated on nocturnal species; even though the analysis of their population trends is a key indicator, there is a gap in the literature addressing their audio-based identification. After compiling a suitable dataset including six nocturnal bird species, this study employs both supervised (k-Nearest Neighbors, Support Vector Machines) and unsupervised (k-means) methods operating in the feature space formed by Mel-spectrograms. We conclude that automatic classification based on k-Nearest Neighbour and Support Vector Machines provide almost excellent results with a recognition rate in the order of 90%.

Keywords

Bird vocalizations Bioacoustics Acoustic ecology Machine learning Audio signal processing Audio pattern recognition 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Michelangelo Acconcjaioco
    • 1
  • Stavros Ntalampiras
    • 1
    Email author
  1. 1.Department of Computer ScienceUniversity of MilanMilanItaly

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