## 5 Conclusions  
In this paper, we proposed a complete conceptual implementation based on the Wireless Acoustic Sensor Network (WASN) for recording, transferring, post-processing, and recognition of noise in order to create and visualize noise maps of acoustic events, as well as to present additional information and noise statistics. We reported the design details, implementation, and real-world deployment of the proposed system. Specifically, we adopted the improved endpoint detection method and compression with ADPCM to reduce the amount of data under the premise of ensuring audio quality and packaged the sensor node for long-term outdoor monitoring. The proposed system not only captures the noise level from each sensor node, but also captures audio data when the noise level reaches a threshold. These data were successfully used for construction of noise map and recognition of acoustic event, presenting the results on a monitoring platform. Most importantly, in Section 4, we reported on the field deployment and analysis of a WASN consisting of 50 sensor nodes in the campus, which indicates that the proposed system has a high accuracy of sound level and well capacity for recognition of the car horn in real life.  
In our future works, we will report a larger deployment with our system in a district of our city, combining the improved model of CNNs to automatically classify multiple species of acoustic events, such as shout, crash, explosion. It is of great significance to improving people's quality of life and ensuring the safety of the living environment. We will research a semi-supervised tensor completion algorithm for inferring noise levels for locations without measurements by sensor nodes. Consequently, the system can be improved by enhancements on both hardware and software.  
[PERSON] has written most of the paper and has pointed out the directions of the experiments; [PERSON] supplemented a part of the paper and participated in the testing and recording of the experiments; [PERSON] wrote the introduction and reviewed the entire paper; [PERSON] applied for funding and pointed out the directions of the research; [PERSON] conceived of and designed the paper and the experiments; [PERSON] analyzed the data of experiments and wrote the conclusion. All authors have read and agreed to the published version of the manuscript.  
This research work was obtained by the subsidization of the National Natural Science Foundation of China (No. 61771151), the Guangxi Keypoint Research and Invention Program (No. AB17292058), the Guangxi Natural Science Foundation (No. 2016 GXNSFBA38014), the Opening Project of Guangxi Key Laboratory of UAV Remote Sensing (No. WRJ2016 KFO1), the China Postdoctoral Science Foundation (No. 2016M602921 XB), the Natural Science Foundation of Guangxi (No. 2019 GXNSFBA245103), and the Guangxi Graduate innovation Project of science and technology (No. YCSW2019139).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WASN & Wireless Acoustic Sensor Network \\ CNNs & Convolutional Neural Networks \\ GIS & Geographic Information System \\
3 GPP & Third Generation Partnership Project \\ PCM & Pulse Code Modulation \\ ADPCM & Adaptive Differential Pulse Code Modulation \\ CPU & Central Processing Unit \\ GPU & Graphic Processing Unit \\ DRAM & Dynamic Random Access Memory \\ IDE & Integrated Development Environment \\ API & Application Programming Interface \\ MFCC & Mel Frequency Cepstrum Coefficient \\ DCT & Discrete Cosine Transform \\ ABS & Acrylonitrile Butadiene Styrene \\ SNR & Signal to Noise Ratio \\ RF & Random Forest \\ \end{tabular}  
KNNK-Nearest Neighbor  
Support Vector Machine  
URLUniform Resource Locator