Tecnología Electrónica, Escuela Politéncia Superior, Universidad de Sevilla, Calle Virgen de África 7, 41012 Sevilla, Spain.
Fonoteca Zoológica, Departamento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales (CSIC), Calle José Gutiérrez Abascal, 2, 28006 Madrid, Spain.
Sensors (Basel). 2021 May 24;21(11):3655. doi: 10.3390/s21113655.
Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.
网络物理系统（CPS）是一种很有前途的范例，可以适应各种应用。基于物联网（IoT）的监控已成为一个具有新挑战的研究领域，需要从中提取有价值的信息。本文提出了一种用于 CPS 执行的深度学习分类声音系统。该系统基于卷积神经网络（CNN），并专注于两种蛙类的不同发声类型。CNN 与使用梅尔频谱图进行声音相结合，被证明是分类环境声音的一种合适工具。所获得的分类结果非常出色（总体准确率为 97.53%），可被视为该系统在分类其他生物声学目标以及分析自然环境中的生物多样性指数方面的非常有前途的应用。本文最后指出，这种涉及低成本和减少计算资源的 CNN 的执行对于监测广阔的自然区域是可行的。CPS 的使用能够在远程 IoT 节点上灵活和动态地配置和部署新的 CNN 更新。
