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Visualization and categorization of ecological acoustic events based on discriminant features

Huancapaza Hilasaca, Liz Maribel ; Gaspar, Lucas Pacciullio ; Ribeiro, Milton Cezar ; Minghim, Rosane

Ecological indicators, 2021-07, Vol.126, p.107316, Article 107316 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    Visualization and categorization of ecological acoustic events based on discriminant features
  • Autor: Huancapaza Hilasaca, Liz Maribel ; Gaspar, Lucas Pacciullio ; Ribeiro, Milton Cezar ; Minghim, Rosane
  • Assuntos: Classification ; Discriminant features ; Feature selection ; Soundscape ecology ; Visualization
  • É parte de: Ecological indicators, 2021-07, Vol.126, p.107316, Article 107316
  • Descrição: •In total we have analyzed 238 features extracted from audio data using three different descriptors.•Our method enables identification of the most discriminant features for target events•We have managed to select, for our targets, indices as Ht, H, ACI, H’, among others.•Our results present high accuracy whenr classifying frogs, birds and insects.•We have evaluated features numerically and in visual space. Although sound classification in soundscape studies are generally performed by experts, the large growth of acoustic data presents a major challenge for performing such task. At the same time, the identification of more discriminating features becomes crucial when analyzing soundscapes, and this occurs because natural and anthropogenic sounds are very complex, particularly in Neotropical regions, where the biodiversity level is very high. In this scenario, the need for research addressing the discriminatory capability of acoustic features is of utmost importance to work towards automating these processes. In this study we present a method to identify the most discriminant features for categorizing sound events in soundscapes. Such identification is key to classification of sound events. Our experimental findings validate our method, showing high discriminatory capability of certain extracted features from sound data, reaching an accuracy of 89.91% for classification of frogs, birds and insects simultaneously. An extension of these experiments to simulate binary classification reached accuracy of 82.64%,100.0% and 99.40% for the classification between combinations of frogs-birds, frogs-insects and birds-insects, respectively.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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