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In bioacoustic recognition approaches, a “flat” classifier is usually trained to recognize several species of anurans, where the number of classes is equal to the number of species. Consequently, the complexity of the classification function increases proportionally with the number of species. To avoid this issue, we propose a “hierarchical” approach that decomposes the problem into three taxonomic...
Bioacoustics signals classification is an important instrument used in environmental monitoring as it gives the means to efficiently acquire information from the areas, which most of the time are unfeasible to approach. To address these challenges, bioacoustics signals classification systems should meet some requirements, such as low computational resources capabilities. In this paper, we propose...
The Amazon Rainforest degradation is a worldwide concern. The rainforest has been endangered by the illegal wood extraction without control even in the preservation areas. Due to the large geography extension prevent these crimes with an unmanned aerial vehicle (UAV) is not always possible. The Wireless Acoustics Sensor Network (WASNs) technology can alleviate this problem. Here, we present an acoustical...
In this work, we evaluate the performance of a distributed classification system in a Wireless Sensor Network for monitoring anurans. Our aim is to study how to take advantage of the collaborative nature of the sensor network to improve the recognition of anuran calls. To accomplish this, we evaluate four low-cost techniques (majority vote, weighted majority vote, arithmetic and geometric combinators)...
Wildlife sounds provide relevant information for non-intrusive environmental monitoring when Wireless Sensor Networks (WSNs) are used. Thus, collecting such audio data, while maximizing the network lifetime, is a key challenge for WSNs. In this work, we propose a methodology that applies Compressive Sensing (CS) aiming at collecting as little data as possible to allow the signal reconstruction, so...
Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, in-network data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the...
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