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

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          Abstract

          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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          Most cited references44

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          Comparing two K-category assignments by a K-category correlation coefficient.

          J Gorodkin (2004)
          Predicted assignments of biological sequences are often evaluated by Matthews correlation coefficient. However, Matthews correlation coefficient applies only to cases where the assignments belong to two categories, and cases with more than two categories are often artificially forced into two categories by considering what belongs and what does not belong to one of the categories, leading to the loss of information. Here, an extended correlation coefficient that applies to K-categories is proposed, and this measure is shown to be highly applicable for evaluating prediction of RNA secondary structure in cases where some predicted pairs go into the category "unknown" due to lack of reliability in predicted pairs or unpaired residues. Hence, predicting base pairs of RNA secondary structure can be a three-category problem. The measure is further shown to be well in agreement with existing performance measures used for ranking protein secondary structure predictions. Server and software is available at http://rk.kvl.dk/.
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            Real-time bioacoustics monitoring and automated species identification

            Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. Given the urgency of climate change and the loss of habitat, it is vital that we use new technologies to improve and expand global biodiversity monitoring to thousands of sites around the world. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON), a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings. The major components of the cyberinfrastructure include: a solar powered remote monitoring station that sends 1-min recordings every 10 min to a base station, which relays the recordings in real-time to the project server, where the recordings are processed and uploaded to the project website (arbimon.net). Along with a module for viewing, listening, and annotating recordings, the website includes a species identification interface to help users create machine learning algorithms to automate species identification. To demonstrate the system we present data on the vocal activity patterns of birds, frogs, insects, and mammals from Puerto Rico and Costa Rica.
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              The use of acoustic indices to determine avian species richness in audio-recordings of the environment

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                30 July 2018
                August 2018
                : 18
                : 8
                : 2465
                Affiliations
                [1 ]Ingeniería del Diseño, Escuela Politécnica Superior, Universidad de Sevilla, 41004 Sevilla, Spain; javier@ 123456romeroyromero.es
                [2 ]Tecnología Electrónica, Escuela Ingeniería Informática, Universidad de Sevilla, 41012 Sevilla, Spain; acarrasco@ 123456us.es (A.C.); jbarbancho@ 123456us.es (J.B.)
                Author notes
                [* ]Correspondence: amalialuque@ 123456us.es ; Tel.: +34-955-420-187
                Author information
                https://orcid.org/0000-0002-1205-4722
                https://orcid.org/0000-0002-6456-7036
                https://orcid.org/0000-0001-9474-3929
                https://orcid.org/0000-0002-9132-6158
                Article
                sensors-18-02465
                10.3390/s18082465
                6111609
                30061506
                57cd5c52-14f1-4c97-bbde-8add75515e9c
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 11 June 2018
                : 27 July 2018
                Categories
                Article

                Biomedical engineering
                habitat monitoring,audio monitoring,sensor network,sound classification
                Biomedical engineering
                habitat monitoring, audio monitoring, sensor network, sound classification

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