## 2 Related Work
### Search Results and Analysis  
The constructed query returned 37 publications from both databases. Following the download of these publications, the application of the PRISMA methodology began with the elimination of duplicates, which resulted in 23 publications that warranted detailed reading. Figure 1 illustrates the results of the application of the PRISMA workflow to this literature review. In this literature review, our primary objective was to investigate the existing research works related to the analysis and mapping of nightlife noise, using ML techniques, in order to comprehend potential patterns. Table 1 summarizes the topics identified in the literature review ordered by the number of documents. As is evident, machine learning, the Internet of Things (IoT), and deep learning play a significant role in noise monitoring and the study and comprehension of noise pollution. Additionally, some studies also address the topics of smart traffic and noise prediction. In ref. [25], [PERSON] et al., investigate the use of ML models to classify environmental sounds that are considered noise pollution in smart cities. Different types of noise were collected in sixteen cities in Malaysia. The best ML sound classification model achieved results with an accuracy score exceeding 0.95. In ref. [26], [PERSON] et al., use an IoT system to monitor noise at a university campus in Spain. Using ML techniques, the authors found that most university community members move through the campus at similar hours, causing congestion and acoustic pollution above regulation limits. In [27], [PERSON] et al. present a similar study, performed in India, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Topic** & **References** & **Number of Documents** \\ \hline Machine Learning & [25; 26; 27; 28; 29; 30; 31; 32; 33] & 9 \\ \hline Noise Pollution & [25; 26; 27; 28; 29; 33; 34; 35; 36] & 9 \\ \hline Noise Monitoring & [8; 19; 30; 31; 32; 33; 34] & 7 \\ \hline Internet of Things & [8; 26; 28; 33; 34; 37] & 6 \\ \hline Deep Learning & [19; 35; 37; 38; 39] & 5 \\ \hline Smart Traffic & [29; 36; 39] & 3 \\ \hline Noise Prediction & [27; 37; 38] & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Topics found in the literature review. Figure 1: PRISMA workflow diagram. aimed to understand the levels of noise pollution during relevant events. The authors proposed an ML model to predict noise pollution, achieving an accuracy higher than 90%. [PERSON] et al. [28] use IoT and ML to better understand and combat noise pollution. Their framework can successfully detect noise levels above tolerable limits. [PERSON] et al. [33] also combine IoT and ML to better inspect and monitor urban noise. In ref. [30], [PERSON] et al., combine ML techniques with a geo-sensing application to help decision makers have as much information as possible. The main function of the framework is to provide real-time information on the acoustic impact on the city and they also provide an ML module capable of predicting the nature of the noise with an acceptable percentage of accuracy. [PERSON] et al. [32] present a robust environmental noise monitoring system that utilizes smartphones and cloud services for high spatial granularity. The architecture efficiently captures noise pollution data through mobile sensors and Firebase technology, achieving substantial energy and computing savings of approximately 60%. While slight measurement variations were noted among different smartphones, they were generally not significant. [PERSON] et al. [8] discuss the use of IoT solutions to help monitor and map noise in smart cities. Cost, accuracy, scalability, reliability and capability are discussed in order to find the best solution. In ref. [34], [PERSON] et al. also use IoT technologies, combined with spatial interpolation techniques and real-world participatory sensing-based datasets collected by participants over a period of one year, to help monitor noise pollution. The authors in refs.