Artificial Intelligence Review

, Volume 49, Issue 3, pp 375–391 | Cite as

Frog call classification: a survey

Article

Abstract

Over the past decade, frog biodiversity has rapidly declined due to many problems including habitat loss and degradation, introduced invasive species, and environmental pollution. Frogs are greatly important to improve the global ecosystem and it is ever more necessary to monitor frog biodiversity. One way to monitor frog biodiversity is to record audio of frog calls. Various methods have been developed to classify these calls. However, to the best of our knowledge, there is still no paper that reviews and summarizes currently developed methods. This survey gives a quantitative and detailed analysis of frog call classification. To be specific, a frog call classification system consists of signal pre-processing, feature extraction, and classification. Signal pre-processing is made up of signal processing, noise reduction, and syllable segmentation. Following signal preprocessing, the next step is feature extraction, which is the most crucial step for improving classification performance. Features used for frog call classification are categorized into four types: (1) time domain and frequency domain features (we classify time domain and frequency domain features into one type because they are often combined together to achieve higher classification accuracy), (2) time-frequency features, (3) cepstral features, and (4) other features. For the classification step, different classifiers and evaluation criteria used for frog call classification are investigated. In conclusion, we discuss future work for frog call classification.

Keywords

Frog call classification Bioacoustics Soundscape ecology Sensor ecology Acoustic ecology 

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jie Xie
    • 1
  • Michael Towsey
    • 1
  • Jinglan Zhang
    • 1
  • Paul Roe
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia

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