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

Abstract

Natural sounds like wind, water, wildlife, and vegetation are considered acoustic sounds and are referred to as the sounds cape. Wildlife sounds usually provide enough data to classify and monitor the fauna. In this context, anurans (frogs and toads) have been used by biologists as early indicators of ecological stress in a given environment. Compressive sensing is a promising technique that can be used to reduce the amount of transmitted data in a wireless sensor network, and, thus, minimize the limited resources of sensors nodes. In this work, we collect samples of the anuran audio using compressive sensing, send them towards the sink node, where the samples are reconstructed and identify the correct anuran specie of each call. Our main goal is to evaluate whether compressive sensing is a viable technique to reconstruct properly the anuran calls and identify the correct specie in an environment monitored by a wireless sensor network. We evaluate the proposed methodology by calculating different sound distances between the original and the reconstructed calls. Results show that sampling only 10% of the original data, the sink node can reconstruct the original audio with a high quality.