Obviously, the combination of environmental noise monitoring and acoustic events recognition are of great significance to analyze the noise pollution and the quality of living environments, raising civic consciousness and formulating corresponding noise reduction strategies. Although previous works on noise assessments and acoustic event recognitions achieve significant progress, they remain impractical in complex urban environments due to the following drawbacks:  
1. The large-scale acoustic sensor networks, especially those deployed for acoustic events recognition, generate a large amount of data from each sensor node, which brings the large pressure on transmission of network. 2. The sounds of interest are superimposed to a significant level of background noise in real-life scenario, which might have an infaust influence on the accuracy of recognition. 3. The limitation of long-term outdoor monitoring: (1) The dependence of the sensor nodes on the restricted power supplies (batteries) is a defect, as it impedes the longevity of the network; (2) the waterproof measures of sensor nodes are not enough to be resistant to extreme outdoor weather. Most importantly, the development of acoustic signal processing techniques to identify noise events based on reliable WASNs in urban areas is an open and new challenge [15]. To address this challenge, this paper presents 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. To address these drawbacks mentioned above, the proposed system mainly includes the following characteristics:  
1. Every acoustic sensor node in our system is powered by a solar panel and is waterproof with the package, which is well-suitable for long-term outdoor monitoring. Of course, it can also be powered by a directly available power if the outdoor power is possible. 2. The combination of endpoint detection and compression with the Adaptive Differential Pulse Code Modulation (ADPCM), which is performed on the embedded processing board, makes it efficient to reduce the amount of data. 3. The acoustic database is made up of live recordings created by sensor nodes, which is helpful in improving recognition accuracy. 4. A novel monitoring platform is designed to intuitively present noise maps, acoustic event information, and noise statistics, which allows users to conveniently get information on the noise situation. Specifically, the proposed system utilizes sensor nodes equipped with various hardware devices such as acoustic sensors, processing boards, and transmission modules to capture the urban noise level, record the acoustic events, and transfer the resulting data to the remote server. Such a server would be used to complete data reception, storage, feature extraction, recognition, as well as monitoring platform construction. We conduct some experiments of sensor nodes and deploy a WASN to process the audio data collected by 50 sensor nodes on campus to validate the performance of the system. The rest of the paper is organized as follows. Section 2 introduces the hardware and software used in the proposed system. Section 3 describes the implementation steps of our system. Section 4 presents the deployment of the proposed system, experimental results, and analysis. Finally, Section 5 concludes the paper.