03509nas a2200217 4500008004100000245007000041210006900111260004000180520274700220653002902967653002102996653001703017653002903034653005303063653003303116653002303149653002603172653003903198100001303237856004103250 2017 eng d00aAcoustic classification of Australian frogs for ecosystem surveys0 aAcoustic classification of Australian frogs for ecosystem survey bQueensland University of Technology3 a

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.

10aAcoustic event detection10aAcoustic feature10abioacoustics10aFrog call classification10aMultiple-instance multiple-label learning (MIML)10aMultiple-label learning (ML)10aSoundscape ecology10aSyllable segmentation10aWavelet packet decomposition (WPD)1 aXie, Jie uhttps://bio.acousti.ca/id/node/5340603307nas a2200313 4500008004100000245010000041210006900141260004000210520226600250653002102516653001802537653001702555653001302572653001202585653001502597653001902612653003302631653001702664653002602681653001302707653004402720653002702764653003402791653002302825653003902848653001802887100002102905856006702926 2018 eng d00aANALYSIS AND VISUALISATION OF VERY-LONG-DURATION ACOUSTIC RECORDINGS OF THE NATURAL ENVIRONMENT0 aANALYSIS AND VISUALISATION OF VERYLONGDURATION ACOUSTIC RECORDIN bQueensland University of Technology3 a

Advances in technology and reduction in data storage costs enable the autonomous collection of large quantities of continuous audio recordings. While the collection of very long environmental recordings has become easier, the analysis of these recordings remains challenging. A very-long-duration audio recording is defined as one with a minimum length of one day, but may have durations of weeks, months, or years. This thesis provides methods for data reduction and visualisation that enable the ecological interpretation and navigation of very-long-duration audio recordings.
The major theme of data reduction commenced after the establishment of protocols and the collection of two thirteen-month continuous audio recordings from two separate Southeast Queensland forest ecosystems. The acoustic indices calculated on one-minute audio segments were used to develop two new techniques to visualise the contents of very- long-duration recordings. An acoustic index is a mathematical expression used to measure a particular aspect of the energy distribution in audio recordings. Microphone failure in one channel was noticed shortly after the recording commenced. A method was established to detect microphone problems in long recordings.
A novel error measure was developed to detect seasonal and site differences and enable optimisation of the clustering based on seasonal and site differences in the data. Cluster interpretation on very-long-duration audio recordings is problematic because listening to large amounts of audio is time-consuming and therefore impractical. To overcome this, a series of five methods were developed to build on the interpretations made through listening. These methods enabled the allocation of an acoustic label to each cluster, resulting in a labelled acoustic sequence. This acoustic sequence was used to develop three additional visualisation techniques.
The culmination of the methods developed in this thesis was the six case studies. These extended the ecological interpretation of the acoustic sequence beyond those that were made through the visualisations. The case studies demonstrated that clustering can facilitate ecological interpretation of very-long-duration audio recordings.

10aacoustic indices10aAnthropophony10abioacoustics10abiophony10aCicadas10aClustering10aData reduction10aDiel plots; Dot-matrix plots10aecoacoustics10aEcological Monitoring10aGeophony10aLong-duration false-colour spectrograms10aMicrophone malfunction10aPrincipal Components Analysis10aSoundscape ecology10aVery-long-duration audio recording10aVisualisation1 aPhillips, Yvonne uhttps://eprints.qut.edu.au/123020/1/Yvonne_Phillips_Thesis.pdf