## 2 Related Works  
In recent years, we have observed a dynamic development of acoustic technologies, supported by artificial intelligence and the Internet of Things (IoT), in medical, industrial, and urban applications. Increasingly, reviews in the literature focus on specific aspects of these issues, ranging from wearable sound sensors in health monitoring to fault detection in transportation systems and acoustic machine diagnostics. Despite numerous valuable studies, there is a lack of papers that cover this topic in a holistic, cross-sectional manner and consider contemporary possibilities resulting from the integration of acoustics, AI, and the IoT. This article fills this gap, presenting a unique and broad view of modern technologies for acquiring and analysing acoustic information, both from a theoretical perspective and from the perspective of real applications. Thanks to this approach, the article provides an overview of the current state of knowledge. It serves as a practical compendium for researchers and engineers seeking a comprehensive understanding of this rapidly evolving field. In [8], [PERSON] et al. provided a comprehensive review of the latest developments in portable and wearable acoustic sensors for monitoring human health. They focused on various technologies for detecting sounds from the body, such as heart, lung, or gastrointestinal sounds, analysing the mechanisms of sensors based on capacitive, piezoelectric, and triboelectric principles, among others. They discussed specific examples of portable devices (for example, electronic stethoscopes) and modern, wearable sensors in skin patches, bright fabrics, or bioinspired MEMSs [9]. They also analysed their application in remote care, diagnostics, and telemedicine. The authors assessed the advantages and limitations of individual solutions. They indicated directions for further research, emphasising the potential of these technologies in precise, non-invasive health monitoring, especially in home conditions and among patients requiring constant supervision. In [10], [PERSON] et al. reviewed the applications of miniature microelectromechanical systems (MEMSs) in medicine, particularly in analysing acoustic signals from the body, such as heart and breathing sounds. They showed that MEMSs are gradually replacing traditional stethoscopes, offering greater precision in recording and analysing small vibrations and pressure changes. The article describes both the principles of operation of these sensors and their advantages (miniaturisation, accuracy, and potential for personalising care), as well as current limitations, including a low signal amplitude, susceptibility to interference, limited patient acceptability when wearing them, and challenges related to production costs and recycling. The authors highlight the need to develop new materials, enhance algorithms for data analysis, and integrate these devices more comprehensively with Internet of Things systems and artificial intelligence. They also point out that developing this technology requires technical progress and building greater trust from the clinical community. In their opinion, the future of MEMS in medicine lies in systems that are more intelligent, comfortable for users, integrated with remote and personalised care, and capable of recording less-studied signals, such as swallowing sounds. In [11], [PERSON] et al. conducted a comprehensive literature review on belt idler fault detection using acoustic and vibration signals and machine learning methods. Idlers are an essential but difficult-to-monitor element of conveyor systems due to their large number and dispersion in the industrial environment. The review encompasses all key stages of the diagnostic process, from data collection, signal processing, and feature extraction to the construction of machine learning models. Various machine learning and deep learning models, such as SVM, KNN, decision trees, ANN, and CNN, are discussed, with a particular emphasis on their effectiveness in detecting early signs of damage.