# Sound Localization Framework for Construction Site Monitoring
## 8 Conclusions  
This study proposed a TDOA technology-based sound localization framework called CSLF by analyzing the spatial characteristics of construction sites. CSLF calculated the arrival delay time using GCC-PHAT and repeatedly calculated the expected coordinate value of the sound source using LSM to estimate the location of sounds. Various tests were conducted in laboratories and construction sites using a development kit (Raspberry Pi) to verify the proposed framework. In the 2D array tests, the triangular array showed better estimation results compared to the square array. Accordingly, a tetrahedral array with the minimum number of microphones (four) was used in the 3D tests. The interior effectiveness of the verification test results showed that the average error was within 0.5 m along each axis, with x-, y-, and z-axes errors of 0.26, 0.27, and 0.36 m, respectively. In the exterior tests conducted at an actual construction site, the framework showed average x-, y-, and z-axes errors of 0.19, 0.21, and 0.24 m, respectively. Compared to the outdoor environment, the indoor environment exhibited a relatively large error range owing to the influence of reverberation and the reflected sound. However, considering that both the indoor and outdoor environments demonstrated an error range of less than 0.5 m and a minimum error range of 0.36 m, the cause or source of sound generation can be easily identified at construction sites. Previous studies that used sound data at construction sites mostly focused on classifying activities through sound pattern analysis under various scenarios. However, in this study, a sound-source localization framework was developed rather than sound data pattern analysis to contribute to developing a system that can be applied in connection with previously researched sound-source classification algorithms and classification algorithms to be researched in the future. This framework can perform localization and site monitoring using noises (sound data) at construction sites that need immediate identification, thereby allowing site managers to perform management and supervision activities more proactively. In addition, it can be used as a supplementary measure to solve problems with environmental factors (lighting, dust, snow, and fog) and privacy, thus, addressing the limitations of existing vision data. The CSLF developed in this study will be capable of identifying the location of abnormal events with sound data at construction sites and leveraging this information to acquire rapid responses from people (workers and managers) or mechanical equipment (CCTV and robots). Additionally, the CSLF can also identify work conditions and check construction productivity through various types of sound analysis at construction sites. We believe this can contribute to the construction management field significantly. As data transmission is made through hardwired connections in the current version of the CSLF, the detection radius is determined by the length of the LAN line. In the future, wireless data transfer-enabled frameworks will be developed by incorporating the proposed framework into IoT to expand the detection range. Additionally, more accurate location estimates would be possible by reducing errors resulting from data delays. Once a cloud computing environment is integrated with IoT devices, it can be installed into any space at the construction site, thereby allowing the monitoring of the situation inside the site in real time. In addition, the CSLF could be integrated with algorithms that use machine learning to understand sound types and determine abnormal events (sounds). For sheathing work, as an example, it will be possible to prevent large accidents, such as the collapse of sheathing and ground, by analyzing the sound of falling building materials or leaking soil at night and judging it as a sign of collapse.