Intelligent feature extraction and classification of anuran vocalizations

Huang, Chenn‐Jung; Chen, You-Jia; Chen, Heng-Ming; Jian, Jui-Jiun; Tseng, Sheng-Chieh; Yang, Yi-Ju; Hsu, Po-An

doi:10.1016/j.asoc.2014.01.030

Cited by 37 publications

(15 citation statements)

References 6 publications

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“…All those features have been used for frog call classification except average power of frequency band [8,14]. All those features are concatenated into together to build a new feature set, whose dimension is 59.…”

Section: Feature Extractionmentioning

confidence: 99%

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Investigation of domain adaptation for acoustic frog species classification

Xie¹

2018

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Acoustic frog species classification has received much attention for its importance in assessing biodiversity. However, most previous frog call classification models are trained and tested using the data collected from the same area, which greatly limits the model's generalization. In practice, frogs often have regional accents. When training and testing data are collected from different areas, there is an adverse impact on frog call classification performance. To tackle this problem, this paper investigates domain adaptation for classifying frog calls collected from different areas. To evaluate the performance of our proposed methods, two frog call datasets, which are collected from subtropical eastern Australia and tropical north-eastern Australia, are used. Experimental results demonstrate that domain adaptation can significantly improve the weighted F1-score from 72.8% to 85.5%.

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Section: Feature Extractionmentioning

confidence: 99%

“…Many authors have proposed various methods for acoustic frog species classification [8,15,14], but more work is needed to address the problem of classifying frog calls collected from different areas. Most previous classification schemes have an assumption that the probability distributions of the training and testing datasets are similar.…”

Section: Introductionmentioning

confidence: 99%

Investigation of domain adaptation for acoustic frog species classification

Xie¹

2018

Preprint

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“…Temporal features for frog call classification have been explored for a long time [Camacho et al, 2013, Chen et al, 2012, Huang et al, 2014a. To achieve a better classification performance, temporal features are often combined with perceptual features for frog call classification.…”

Section: Temporal and Perceptual Features For Frog Call Classificationmentioning

confidence: 99%

“…Techniques used for frog signal processing include STFT [Colonna et al, 2015, Huang et al, 2014a, WPD [Yen and Fu, 2002], and DWT [Colonna et al, 2012b]. STFT is the most widely used technique due to its flexible implementation and better applicability.…”

Section: Signal Processingmentioning

confidence: 99%

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Acoustic classification of Australian frogs for ecosystem survey

Xie¹

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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 conservation 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 improving 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 decomposition (WPD) to improve cepstral feature's anti-noise ability for frog call classification using both trophy and field recordings (Chapter 4). v (3) Design a novel multiple-instance multiple-label (MIML) framework to classify multiple simultaneously vocalising frog species in field recordings (Chapter 5).(4) Design a novel multiple-label (ML) framework to increase the robustness of classification 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 temporal scales can be surveyed, which can help ecologists better understand the ecosystem.vi Keywords

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