用于城市声音分类的低成本无线声传感器网络

IF 0.6 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Davide Salvo, G. Piñero, P. Arce, Alberto González
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引用次数: 2

摘要

在本文中,我们提出了一种无线声传感器网络(无线声传感器网络),它可以识别来自城市环境的一组声音事件或类别。was的节点是树莓派设备,不仅可以记录环境声音,还可以通过深度卷积神经网络(CNN)处理和识别声音事件。据我们所知,这是第一个在低成本设备上运行CNN分类器的nn。此外,该网络是根据开放标准FIWARE设计的,因此整个系统无需专有软件或特定硬件即可复制。尽管与其他通过云计算或边缘计算执行分类的WASN相比,我们的低成本nn实现了相似的准确性,但我们的问题是深度学习算法所需的高计算负载,即使在测试模式下也是如此。此外,wasn被设计为不断监测环境,在我们的例子中,这意味着不断分类“背景声音”。我们建议在CNN分类之前引入预检测阶段,以节省功耗。在我们的例子中,无线网络被放置在一个大的大道上,其中的“背景声音”事件是通常的交通噪音,我们希望检测到其他声音事件,如喇叭,警报器或非常大的声音。我们设计了一个预检测阶段,仅在可能发生与流量不同的事件时激活分类器。为此,计算了基于声压级的两个参数,并与两个相应的阈值进行了比较。在瓦伦西亚市进行的实验结果表明,由于预检测阶段,树莓派CPU的使用率降低了六倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Low-cost Wireless Acoustic Sensor Network for the Classification of Urban Sounds
We present in this paper a wireless acoustic sensor network (WASN) that recognizes a set of sound events or classes from urban environments. The nodes of the WASN are Raspberry Pi devices that not only record the ambient sound, but they also process and recognize a sound event by means of a deep convolutional neural network (CNN). To our knowledge, this is the first WASN running a CNN classifier over low-cost devices. Moreover, the network has been designed according to the open standard FIWARE, so the whole system can be replicated without the need of proprietary software or specific hardware. Although our low-cost WASN achieves similar accuracy compared to other WASNs that perform the classification through cloud or edge computing, our problem is the high computation load required by deep learning algorithms, even in testing mode. Moreover, the WASNs are designed to be constantly monitoring the ambient, which in our case means constantly classifying the "background sound''. We propose here to introduce a pre-detection stage prior to the CNN classification in order to save power consumption. In our case, the WASN is placed in a big avenue where the "background sound'' event is the usual traffic noise, and we want to detect other sound events as horns, sirens or very loud sounds. We have designed a pre-detection stage that activates the classifier only when an event different from traffic is likely occurring. For this purpose, two parameters based on the sound pressure level are computed and compared with two corresponding thresholds. Experimental results have been carried out with the proposed WASN in the city of Valencia, achieving a six-times reduction of the Raspberry Pi CPU's usage due to the pre-detection stage.
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来源期刊
Ad Hoc & Sensor Wireless Networks
Ad Hoc & Sensor Wireless Networks 工程技术-电信学
CiteScore
2.00
自引率
44.40%
发文量
0
审稿时长
8 months
期刊介绍: Ad Hoc & Sensor Wireless Networks seeks to provide an opportunity for researchers from computer science, engineering and mathematical backgrounds to disseminate and exchange knowledge in the rapidly emerging field of ad hoc and sensor wireless networks. It will comprehensively cover physical, data-link, network and transport layers, as well as application, security, simulation and power management issues in sensor, local area, satellite, vehicular, personal, and mobile ad hoc networks.
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