## 4 Applications of Intelligent Acoustic Monitoring
### Infrastructure and Road Quality  
This subsection focuses on monitoring the quality of road surfaces using acoustic sensors, in the context of road surface diagnostics [129] and road noise modelling [130].  
In [129], [PERSON] et al. developed an innovative system for monitoring road surface conditions in smart cities, based on acoustic analysis and artificial intelligence. The proposed solution consisted in placing a sensor equipped with a microphone and an ultrasonic module in the rim of the vehicle wheel, which collected sound data and information about the depth of road surface damage. The system classifies the road as smooth, slippery, grassy, or uneven, and the data were analysed using machine learning algorithms such as MLP, SVM, RF, and kNN. The highest efficiency was achieved thanks to the MLP model, which achieved an accuracy of 98.98%. The distinguishing feature of the system was its practical, hardware nature and the ability to automatically inform road services about the location and nature of road surface damage. The authors suggested that the system may support vehicle-to-vehicle communication (V2V) in the future, enabling warnings about poor road conditions and the dynamic designation of alternative routes, which can significantly enhance city safety and traffic flow.  
In [130], [PERSON] et al. presented a new road noise estimation model (RTNM) that enabled the dynamic prediction of noise levels in smart cities based on real-time traffic intensity and speed data. The model integrated their developed noise emission model for light vehicles (VNSP), taking into account differences resulting from the type of vehicle (diesel, petrol, hybrid, LPG), with the CNOSSOS model (for heavy vehicles) and with a sound propagation model that converted sound power levels (Lw) to sound pressure levels (LAeq) at the reception points. The input data came from a radar in Aveiro (Portugal), and the model results were compared with data from a professional noise sensor. The results showed good agreement with reality (the average error did not exceed 5.8%), although the model slightly overestimated noise during rush hours and underestimated it at night. This suggested the need to incorporate background noise and vehicle acceleration data in future studies. The proposed solution was scalable, could be supported by data from low-cost sensors, and provided valuable information to city decision-makers in noise management. The authors emphasised the potential for further model development, including the addition of more vehicle types, detailed micro-motion data, and corrections for terrain slope and acceleration manoeuvres.  
These works demonstrate the practical potential of acoustics in road infrastructure management. Integration with vehicles (V2V, VANET) creates new possibilities for automatic and dynamic response to faults.  
Summary and Comparison  
The mentioned applications showed how acoustic monitoring is being employed in many varied fields, yet some patterns are repeated. In smart cities and public safety, the primary goals are speed, reliability, and the ability to utilise common devices (such as smartphones) for large-scale deployment. In healthcare and industry, the focus shifts to accuracy, non-invasiveness, and the ability to operate in loud or confined spaces. Environmental systems vary in type and are placed in different areas. On the other hand, they value long-term monitoring, low cost, and little maintenance. A unifying thread in all of these areas is the increasing use of lightweight machine learning models and edge computing to make decisions in real time and close to the source. We are witnessing the growth of distributed acoustic intelligence, which is flexible, adaptable, and becoming increasingly autonomous, rather than relying on isolated systems.