Bioacoustic signal classification in continuous recordings: Syllable-segmentation vs sliding-window

Expert Systems with Applications - Tập 152 - Trang 113390 - 2020
Jie Xie1,2,3, Kai Hu2,3, Mingying Zhu4, Ya Guo2,3
1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China
2Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Jiangnan University, Wuxi214122, PR China
3School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China
4Department of Economics, University of Ottawa, Ontario K1N6N5, Canada

Tài liệu tham khảo

Abdoli, 2019, End-to-end environmental sound classification using a 1d convolutional neural network, CoRR, abs/1904.08990 Aguilar, 2007, Accuracy assessment of digital elevation models using a non-parametric approach, International Journal of Geographical Information Science, 21, 667, 10.1080/13658810601079783 Breiman, 2001, Random forests, Machine learning, 45, 5, 10.1023/A:1010933404324 Colonna, 2016, Automatic classification of anuran sounds using convolutional neural networks, 73 Colonna, 2016, How to correctly evaluate an automatic bioacoustics classification method, 37 Colonna, 2018, Feature evaluation for unsupervised bioacoustic signal segmentation of anuran calls, Expert Systems with Applications, 106, 107, 10.1016/j.eswa.2018.03.062 Dai, 2017, Very deep convolutional neural networks for raw waveforms, 421 Eliza, 2019, Automatic recognition of frog vocalizations using jetstream, 4 Feng, 2019, Myocardial infarction classification based on convolutional neural network and recurrent neural network, Applied Sciences, 9, 1879, 10.3390/app9091879 Gillespie, 2015, Rapid decline and extinction of a Montane frog population in southern australia follows detection of the amphibian pathogen batrachochytrium dendrobatidis, Animal Conservation, 18, 295, 10.1111/acv.12174 Han, 2011, Acoustic classification of australian anurans based on hybrid spectral-entropy approach, Applied Acoustics, 72, 639, 10.1016/j.apacoust.2011.02.002 Harma, 2003, Automatic identification of bird species based on sinusoidal modeling of syllables, 5, V Hoshen, 2015, Speech acoustic modeling from raw multichannel waveforms, 4624 Huang, 2014, Intelligent feature extraction and classification of anuran vocalizations, Applied Soft Computing, 19, 1, 10.1016/j.asoc.2014.01.030 Huang, 2009, Frog classification using machine learning techniques, Expert Systems with Applications, 36, 3737, 10.1016/j.eswa.2008.02.059 Jaafar, 2013, Automatic syllables segmentation for frog identification system, 224 Jie Xie, 2015, Acoustic classification of Australian anurans using syllable features, 1 Kim, 2018, Sample-level CNN architectures for music auto-tagging using raw waveforms, 366 Lee, 2006, Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis, Pattern Recognition Letters, 27, 93, 10.1016/j.patrec.2005.07.004 Luque, 2018, Non-sequential automatic classification of anuran sounds for the estimation of climate-change indicators, Expert Systems with Applications, 95, 248, 10.1016/j.eswa.2017.11.016 Noda, 2016, Methodology for automatic bioacoustic classification of anurans based on feature fusion, Expert Systems with Applications, 50, 100, 10.1016/j.eswa.2015.12.020 Ravanelli, 2018, Speaker recognition from raw waveform with sincnet, 1021 Raza, 2019, Heartbeat sound signal classification using deep learning, Sensors, 19, 4819, 10.3390/s19214819 Sainath, 2015, Learning the speech front-end with raw waveform CLDNNS, 1 Stewart, D. (1999). Australian frog calls: subtropical east. Audio CD. http://www.naturesound.com.au/cd_frogsSE.htm. Strout, 2017, Anuran call classification with deep learning, 2662 Thakur, 2017, Rényi entropy based mutual information for semi-supervised bird vocalization segmentation, 1 Tomasini, 2017, Automated robust anuran classification by extracting elliptical feature pairs from audio spectrograms, 2517 Towsey, 2012, A toolbox for animal call recognition, Bioacoustics : The International Journal of Animal Sound and its Recording, 21, 107, 10.1080/09524622.2011.648753 Wimmer, 2013, Analysing environmental acoustic data through collaboration and automation, Future Generation Computer Systems, 29, 560, 10.1016/j.future.2012.03.004 Xie, 2016, Acoustic classification of australian frogs based on enhanced features and machine learning algorithms, Applied Acoustics, 113, 193, 10.1016/j.apacoust.2016.06.029 Zeghidour, 2018, End-to-end speech recognition from the raw waveform, 781 Zhao, 2019, Speech emotion recognition using deep 1d and 2d CNN LSTM networks, Biomedical Signal Processing and Control, 47, 312, 10.1016/j.bspc.2018.08.035 Zhu, 2016, Learning multiscale features directly from waveforms, 1305