Anuran call classification with deep learning

Strout, Julia; Rogan, Bryce; Seyednezhad, S. M. Mahdi; Smart, Katrina; Bush, Mark B.; Ribeiro, Eraldo

doi:10.1109/icassp.2017.7952639

Cited by 20 publications

(19 citation statements)

References 8 publications

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“…Therefore, gamma spectrogram is selected for the subsequent analysis. , CaffeNet is the worst, which is in consistent with [16].…”

Section: Rfsupporting

confidence: 75%

“…Multi-label learning Frog species Different from [15], we use a deep learning algorithm as a feature extractor. In [16], a pre-trained network is found to achieve higher classification accuracy than training a new network. Also, there are only 342 10-s recordings for the experiment, which are not enough for training.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Compared to hand-crafted features, recent use of deep learnings has achieved stateof-the-art accuracy in frog call classification [15], [16], but all recordings used are assumed to have a single species.…”

Section: Introductionmentioning

confidence: 99%

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Multi-label classification of frog species via deep learning

Xie¹

2017

Preprint

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Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into audio clips (10 seconds). Then, various time-frequency representations are generated for each 10-s recording. Next, instead of using traditional hand-crafted features, a deep learning algorithm is used to find the most important feature. Finally, a binary relevance based multi-label classification approach is proposed to classify simultaneously vocalizing frog species with our proposed features. Experimental results show that our proposed features extracted using deep learning can achieve better classification performance when compared to hand-crafted features for frog call classification.

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“…Therefore, gamma spectrogram is selected for the subsequent analysis. , CaffeNet is the worst, which is in consistent with [16].…”

Section: Rfsupporting

confidence: 75%

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-label classification of frog species via deep learning

Xie¹

2017

Preprint

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“…At present, deep learning techniques are being employed in frog acoustics classification [23][24][25], applying convolutional neural networks (CNN). However, most of these works also use MFCC as parameters, relying on the discriminatory capacity of the classifier without looking for a better representation of the acoustic signal information.…”

Section: Reptiles and Amphibiansmentioning

confidence: 99%

A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans

Noda¹,

Sánchez-Rodríguez²,

González³

2018

Reptiles and Amphibians

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Nowadays, human activity is considered one of the main risk factors for the life of reptiles and amphibians. The presence of these living beings represents a good biological indicator of an excellent environmental quality. Because of their behavior and size, most of these species are complicated to recognize in their living environment with image devices. Nevertheless, the use of bioacoustic information to identify animal species is an efficient way to sample populations and control the conservation of these living beings in large and remote areas where environmental conditions and visibility are limited. In this chapter, a novel methodology for the identification of different reptile and anuran species based on the fusion of Mel and Linear Frequency Cepstral Coefficients, MFCC and LFCC, is presented. The proposed methodology has been validated using public databases, and experimental results yielded an accuracy above 95% showing the efficiency of the proposal.

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“…State of the art classification of sound relies on Convolutional Neural Networks (CNN) that take input from some form of the spectrogram [36] or even the raw waveform [37]. Moreover, CNN deep learning approaches have also been used in the identification of anuran sound [38]. In spite of that, studying and optimizing the process of extracting MFCC features is of great interest at least for three reasons.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Symmetry of Integral Transforms for Featuring Anuran Calls

et al. 2019

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The application of machine learning techniques to sound signals requires the previous characterization of said signals. In many cases, their description is made using cepstral coefficients that represent the sound spectra. In this paper, the performance in obtaining cepstral coefficients by two integral transforms, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT), are compared in the context of processing anuran calls. Due to the symmetry of sound spectra, it is shown that DCT clearly outperforms DFT, and decreases the error representing the spectrum by more than 30%. Additionally, it is demonstrated that DCT-based cepstral coefficients are less correlated than their DFT-based counterparts, which leads to a significant advantage for DCT-based cepstral coefficients if these features are later used in classification algorithms. Since the DCT superiority is based on the symmetry of sound spectra and not on any intrinsic advantage of the algorithm, the conclusions of this research can definitely be extrapolated to include any sound signal.

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Anuran call classification with deep learning

Cited by 20 publications

References 8 publications

Multi-label classification of frog species via deep learning

Multi-label classification of frog species via deep learning

A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans

Exploiting the Symmetry of Integral Transforms for Featuring Anuran Calls

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