Non-sequential automatic classification of anuran sounds for the estimation of climate-change indicators

Luque, A.; Romero-Lemos, Javier; Carrasco, Alejandro; Barbancho, Julio

doi:10.1016/j.eswa.2017.11.016

Cited by 28 publications

(23 citation statements)

References 38 publications

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“…For the features' selection stage, we examined various types of feature extraction methods that have been adopted by similar works [1][2][3][4][5][6][7][8][9] in the literature for animal classification purposes. These features' extraction techniques can be divided into the following four categories:…”

Section: Features' Extractionmentioning

confidence: 99%

“…In WASN, the success of an approach implementing these tasks depends on a sensor's capability to perform accurate target recognition and to collaborate with other sensors to determine its location with minimal energy consumption. Whereas some previous works addressed acousticbased target recognition, [1][2][3][4][5][6][7][8][9] other works studied approaches for target localization in WSN. [10][11][12][13][14][15][16][17][18][19][20][21][22] However, a solution that efficiently combines these capabilities in WASN has not yet been designed.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks

Algobail

Soudani

Al-Ahmadi

2019

International Journal of Distributed Sensor Networks

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The development of wireless acoustic sensor networks has driven the use of acoustic signals for target monitoring. Most monitoring applications require continuous network connectivity and data transfers, which can rapidly exhaust nodes’ energy. Consequently, sensors must collaborate in an adequate architecture to perform target recognition and localization tasks and then to send the results to a remote server with a reduced data volume. The design of an energy-efficient scheme that achieves acoustic target recognition and localization remains an open research problem. Accordingly, this article proposes a low-energy acoustic-based sensing scheme for target recognition and localization to be implemented in a cluster-based sensing approach designed to appropriately balance energy consumption and local processing performed by sensor nodes. A reduced set of low-complexity feature extraction methods in the time domain signal are used in the recognition process. The scheme uses the received energy of the acoustic signals for the target localization. This article details the network architecture, the scheme specification, and its implementation. The results show that the scheme can classify targets with 81.34% accuracy. It requires 3.2 mJ of energy when executed in MICAz, achieving 99% energy savings compared to streaming 3 s of an acoustic signal to a remote server.

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Section: Features' Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks

Algobail

Soudani

Al-Ahmadi

2019

International Journal of Distributed Sensor Networks

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show abstract

“…Furthermore, in this field, symmetry is playing an interesting role either as a subject of study, in the form of machine learning algorithms to discover symmetries [14] or as a means to improve the results obtained by automatic recognition systems [15]. Let us emphasize this point: not only can knowing the symmetry of a certain computer algorithm be intrinsically rewarding since it sheds light on the behaviour of the algorithm but it can also be very useful for its interpretation, its optimization or as a in recent research, we have employed a symmetric criterion to select the best feature-extraction procedures (Discrete Cosine Transform versus Discrete Fourier Transform) [16] in an application of the classification of sounds [17,18] effectively deployed in a Wireless Sensor Network as shown in Figure 1. Another examples of industrial applications using classification of sounds can be found in Refs.…”

Section: Introductionmentioning

confidence: 99%

Exploring Symmetry of Binary Classification Performance Metrics

et al. 2019

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Selecting the proper performance metric constitutes a key issue for most classification problems in the field of machine learning. Although the specialized literature has addressed several topics regarding these metrics, their symmetries have yet to be systematically studied. This research focuses on ten metrics based on a binary confusion matrix and their symmetric behaviour is formally defined under all types of transformations. Through simulated experiments, which cover the full range of datasets and classification results, the symmetric behaviour of these metrics is explored by exposing them to hundreds of simple or combined symmetric transformations. Cross-symmetries among the metrics and statistical symmetries are also explored. The results obtained show that, in all cases, three and only three types of symmetries arise: labelling inversion (between positive and negative classes); scoring inversion (concerning good and bad classifiers); and the combination of these two inversions. Additionally, certain metrics have been shown to be independent of the imbalance in the dataset and two cross-symmetries have been identified. The results regarding their symmetries reveal a deeper insight into the behaviour of various performance metrics and offer an indicator to properly interpret their values and a guide for their selection for certain specific applications.

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“…In our previous work, several classifiers for anuran calls are proposed that use non-sequential procedures [30] or temporally-aware algorithms [31], or that consider score series [32], mainly using a set of MPEG-7 features [33]. MPEG-7 is an ISO/IEC standard developed by MPEG (Moving Picture Experts Group).…”

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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Non-sequential automatic classification of anuran sounds for the estimation of climate-change indicators

Cited by 28 publications

References 38 publications

Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks

Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks

Exploring Symmetry of Binary Classification Performance Metrics

Exploiting the Symmetry of Integral Transforms for Featuring Anuran Calls

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