On the Use of Compressive Sensing for the Reconstruction of Anuran Sounds in a Wireless Sensor Network

Diaz, Javier J. M.; Nakamura, Eduardo F.; Yehia, Hani Camille; Salles, Juliana; Loureiro, Antônio A. F.

doi:10.1109/greencom.2012.64

Cited by 5 publications

(8 citation statements)

References 20 publications

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“…There are a large volume of audio monitoring systems in the literature [4,6,7,8,9,10,11,12,13,14]. In the early stage, biologists have traditionally deployed audio recording systems over the natural environment where their research projects were developed [6,7].…”

Section: Introductionmentioning

confidence: 99%

A multi-view CNN-based acoustic classification system for automatic animal species identification

Zhang

Yao

et al. 2020

Ad Hoc Networks

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Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals.

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Section: Introductionmentioning

confidence: 99%

A multi-view CNN-based acoustic classification system for automatic animal species identification

Zhang

Yao

et al. 2020

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“…It is then possible to perform a real-time processing in local mode [ 8 ] or to do so it in a remote center. This second option requires an appropriate communication system; usually a wireless sensor network (WSN), which also often includes information compression technologies [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks

Sendra

Romero-Lemos

Carrasco

et al. 2018

Sensors

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The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames, while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application.

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“…The first step in biological species identification involves the recording of different sounds in their natural environment, where different devices can be used. Processing of the recorded sounds can be performed either locally in real time ( Aide et al, 2013 ), or in a remote centre requiring, in this case, a suitable communication system, usually a wireless sensor network, which generally requires information-compressing technologies ( Diaz et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Temporally-aware algorithms for the classification of anuran sounds

Luque

Romero-Lemos

Carrasco

et al. 2018

PeerJ

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Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts.

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On the Use of Compressive Sensing for the Reconstruction of Anuran Sounds in a Wireless Sensor Network

Cited by 5 publications

References 20 publications

A multi-view CNN-based acoustic classification system for automatic animal species identification

A multi-view CNN-based acoustic classification system for automatic animal species identification

Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks

Temporally-aware algorithms for the classification of anuran sounds

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