Abstract
Fast environmental variations due to climate change can cause mass decline or even extinctions of species, having a dramatic impact on the future of biodiversity. During the last decade, different approaches have been proposed to track and monitor endangered species, generally based on costly semi-automatic systems that require human supervision adding limitations in coverage and time. However, the recent emergence of Wireless Acoustic Sensor Networks (WASN) has allowed non-intrusive remote monitoring of endangered species in real time through the automatic identification of the sound they emit. In this work, an FPGA-based WASN centralized architecture is proposed and validated on a simulated operation environment. The feasibility of the architecture is evaluated in a case study designed to detect the threatened Botaurus stellaris among other 19 cohabiting birds species in The Parc Natural dels Aiguamolls de l’Empordà, showing an averaged recognition accuracy of 91% over 2h 55’ of representative data. The FPGA-based feature extraction implementation allows the system to process data from 30 acoustic sensors in real time with an affordable cost. Finally, several open questions derived from this research are discussed to be considered for future works.
Highlights
Biodiversity describes the full range of different varieties of life on Earth and their diversity.It is a measure a measure of the number and variability of organisms present in different ecosystems.especially due to climate change, fast environmental changes can cause mass decline or even extinctions of species [1]
We have taken into account the real-time implementation of the described birdsong recognition system, and several key issues related to signal processing and the improvement of the machine learning algorithm, as well as ideas to take into account for the FPGA-based Wireless Acoustic Sensor Networks (WASN)
This approach offers the following benefits with respect to a distributed architecture: (a) the power consumption and (b) the cost of the nodes can be lower because each can be implemented with a single micro-controller instead of applying the signal processing algorithm over the raw data in each node using a microprocessor or FPGAs that has been implemented previously in a distributed architecture in the literature, due to the fact that the digital signal processing can be computed in the embedded platform
Summary
Biodiversity describes the full range of different varieties of life on Earth and their diversity.It is a measure a measure of the number and variability of organisms present in different ecosystems.especially due to climate change, fast environmental changes can cause mass decline or even extinctions of species [1]. Biodiversity describes the full range of different varieties of life on Earth and their diversity. It is a measure a measure of the number and variability of organisms present in different ecosystems. The bioacoustic study of specific species and locations may become a useful tool to evaluate the effect of climate change and habitats modification [3,4]. In this context, the acoustic monitoring of birds has been a widely studied area in bioacoustics through the application of sound recognition techniques [5]. Recent technological advances such as Wireless Acoustic Sensor Networks (WASNs) have appeared as an alternative to improve, expand and/or minimize the costs derived from biodiversity
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