## 1 Introduction  
Ongoing urbanization trends have led to the rapid growth of cities, bringing numerous benefits but also introducing challenges, such as noise pollution [1]. This is a significant environmental concern, affecting the health and well-being of city residents [2]. Urban areas are characterized by diverse noise sources, including transportation, industrial activities, construction, and social events. The acoustic environment of a city is highly dynamic, with noise patterns changing across different times and locations [3]. Therefore, understanding and effectively managing urban noise pollution requires innovative approaches that capture the complexity of these patterns [4], such as utilizing machine learning techniques and wireless acoustic sensor networks, as proposed in [5]. The motivation behind this work stems from the need to develop comprehensive and adaptable strategies for assessing and managing noise pollution in cities. Traditional methods of noise monitoring often involve stationary noise sensors placed in specific locations, which may fail to capture the full scope of noise variability across a city. Furthermore, these methods can be costly to deploy and maintain. The trajectory from noise mapping to action plans and eventual mitigation measures represents a crucial avenue in environmental acoustics, particularly in the context of the evolving landscape of smart and sustainable cities. A paradigm shift has been observed, especially in noise mapping, where innovative solutions are emerging, such as real-time evaluation, enhanced monitoring stations with stringent control measures, and the integration of artificial intelligence applications. Noteworthy advancements include mitigations facilitated by electric vehicles, the integration of low-noise pavements, refined input methodologies, and their real-world contextual evaluations [4; 6; 7; 8; 9]. Recent literature underscores the multifaceted developments in wireless acoustic sensor networks for environmental noise monitoring within smart cities [7]. The incorporation of machine learning methods and camera images in intelligent transportation systems (ITS) is contributing to optimized noise maps and action plans [6]. Meanwhile, the integration of the Internet of Things (IoT) for noise mapping in smart cities is a focal point, outlining the state of the art and future directions [7]. Acoustic beamforming algorithms are making strides, with applications in environmental noise [10; 11]. Additionally, researchers are defining key performance indicators for noise monitoring networks [12] and utilizing statistical pass-by methods for unattended road traffic noise measurement [13]. In the realm of electric vehicles, there is a dedicated focus on predicting noise emissions, with advancements in models and coefficients [14; 15]. Artificial neural networks are employed for predicting annoyance evaluations of electric vehicle noise [16], and machine learning techniques are leveraged for real-time air quality and environmental noise detection [17]. Ensemble models based on artificial intelligence find applications in predicting vehicular traffic noise [18], and deep learning and gradient boosting are making strides in urban environmental noise monitoring in smart cities [19]. The comprehensive landscape presented in these studies underscores the dynamic and interdisciplinary nature of noise research in smart cities. On the other hand, mobile phones, being an integral part of modern urban life, offer a ubiquitous, unobtrusive and cost-effective means to collect large volumes of data from diverse areas within a city. By harnessing the capabilities of mobile phones, it is possible to obtain insights into the temporal and spatial dynamics of urban noise pollution that were previously challenging to achieve [20]. Integrating mobile phone data into noise analysis holds the promise of revolutionizing how noise pollution in urban environments is understood, monitored, and addressed.