Acoustic classification of Australian frogs for ecosystem surveys

Frogs play an important role in Earth’s ecosystem, but the decline of their population has been spotted at many locations around the world. Monitoring frog activity can assist con- servation efforts, and improve our understanding of their interactions with the environment and other organisms. Traditional observation methods require ecologists and volunteers to visit the field, which greatly limit the scale for acoustic data collection. Recent advances in acoustic sensors provide a novel method to survey vocalising animals such as frogs. Once sensors are successfully installed in the field, acoustic data can be automatically collected at large spatial and temporal scales. For each acoustic sensor, several gigabytes of compressed audio data can be generated per day, and thus large volumes of raw acoustic data are collected. To gain insights about frogs and the environment, classifying frog species in acoustic data is necessary. However, manual species identification is unfeasible due to the large amount of collected data, and enabling automated species classification has become very important. Previous studies on signal processing and machine learning for frog call classification often have two limitations: (1) the recordings used to train and test classifiers are trophy recordings ( signal-to-noise ratio (SNR) (≥ 15 dB); (2) each individual recording is assumed to contain only one frog species. However, field recordings typically have a low SNR (< 15 dB) and contain multiple simultaneously vocalising frog species. This thesis aims to address two limitations and makes the following contributions.
(1) Develop a combined feature set from temporal, perceptual, and cepstral domains for im- proving the state-of-the-art performance of frog call classification using trophy recordings (Chapter 3).
(2) Propose a novel cepstral feature via adaptive frequency scaled wavelet packet decompo- sition (WPD) to improve cepstral feature’s anti-noise ability for frog call classification using both trophy and field recordings (Chapter 4).
(3) Design a novel multiple-instance multiple-label (MIML) framework to classify multiple simultaneously vocalising frog species in field recordings (Chapter 5).
(4) Designanovelmultiple-label(ML)frameworktoincreasetherobustnessofclassification results when classifying multiple simultaneously vocalising frog species in field recordings (Chapter 6).

Our proposed approaches achieve promising classification results compared with previous studies. With our developed classification techniques, the ecosystem at large spatial and tem- poral scales can be surveyed, which can help ecologists better understand the ecosystem.