A Convolutional Neural Networks Approach to Audio Classification for Rainfall Estimation

被引：0
|
作者：
Avanzato, Roberta [1 ]
Beritelli, Francesco [1 ]
Di Franco, Francesco [1 ]
Puglisi, Valerio Francesco [1 ]
机构：
[1] Univ Catania, Dept Elect Elect & Comp Engn, Catania, Italy
来源：
PROCEEDINGS OF THE 2019 10TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS - TECHNOLOGY AND APPLICATIONS (IDAACS), VOL. 1 | 2019年
关键词：
Audio classification; Enviroment sound classification; Feature extraction techniques; Convolutional Neural Networks; Rainfall Estimation;
D O I：
10.1109/idaacs.2019.8924399
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The recent climatic changes imply an increasing manifestation of calamitous phenomena related to important hydrogeological disruptions in many parts of the earth. For this reason, an accurate estimate of rainfall levels becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. This paper proposes an approach based on Convolutional Neural Networks (CNN) to the classification of the audio signal coming from a new rainfall system.
引用
收藏
页码：285 / 289
页数：5
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