Next Article in Journal
Traffic Accident Prediction Based on Multivariable Grey Model
Next Article in Special Issue
Anti-Shake HDR Imaging Using RAW Image Data
Previous Article in Journal
Ontology-Mediated Historical Data Modeling: Theoretical and Practical Tools for an Integrated Construction of the Past
Open AccessArticle

An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks

Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of an earlier conference paper: “Avanzato, R.; Beritelli, F.; Di Franco, F.; Puglisi, V.F. A Convolutional Neural Networks Approach to Audio Classification for Rainfall Estimation. In Proceedings of the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 18–21 September 2019”.
Information 2020, 11(4), 183; https://doi.org/10.3390/info11040183
Received: 5 March 2020 / Revised: 26 March 2020 / Accepted: 27 March 2020 / Published: 28 March 2020
(This article belongs to the Special Issue Computational Intelligence for Audio Signal Processing)
An accurate estimate of rainfall levels is fundamental in numerous application scenarios: weather forecasting, climate models, design of hydraulic structures, precision agriculture, etc. An accurate estimate becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for estimating rainfall levels present numerous critical issues. The algorithm applies the Convolution Neural Network (CNN) directly to the audio signal, using 3 s sliding windows with an offset of only 100 milliseconds. Therefore, by using low cost and low power hardware, the proposed algorithm allows implementing critical high rainfall event alerting mechanisms with short response times and low estimation errors. More specifically, this paper proposes a new approach to rainfall estimation based on the classification of different acoustic timbres that rain produces at different intensities and on CNN. The results obtained on seven classes ranging from “No rain” to “Cloudburst” indicate an average accuracy of 75%, which rises to 93% if the misclassifications of the adjacent classes are not considered. Some application contexts concern smart cities for which the integration of an audio sensor inside the luminaire of a street lamp is foreseen, precision agriculture, as well as highway safety, by minimizing the risks of aquaplaning. View Full-Text
Keywords: audio pattern recognition and classification; rainfall estimation; audio signal processing; smart audio sensors; convolutional neural networks (CNN) audio pattern recognition and classification; rainfall estimation; audio signal processing; smart audio sensors; convolutional neural networks (CNN)
Show Figures

Figure 1

MDPI and ACS Style

Avanzato, R.; Beritelli, F. An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks. Information 2020, 11, 183.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop