## 7 Discussion and Comparative Analysis  
The analysis of recent studies presented in the section Applications of Intelligent Acoustic Monitoring reveals a growing interest in using AI-based acoustic methods for a wide range of practical applications. Although the works differ in scope, technology, and purpose, several recurring patterns and comparative insights emerge. First, many systems rely on Mel-Frequency Cepstral Coefficients (MFCCs) as a fundamental feature extraction method, regardless of the application domain. This is observed inworks related to UAV fault detection [126], vehicle diagnostics [127], and voice disorder detection [119]. MFCCs are a lightweight and effective representation of sound, enabling high classification accuracy even with limited computing resources. Second, the accuracy of classification models across studies is generally very high. For instance, [PERSON] et al. reported up to 100% accuracy for UAVs with fewer motors, while [PERSON] et al. achieved over 99% accuracy in detecting voice disorders. This suggests that well-preprocessed acoustic data, paired with classical machine learning methods such as SVM, can be highly effective without resorting to deep neural networks, which are often more computationally demanding. In contrast, more complex urban environments, such as those addressed in the works of [PERSON] et al. [118] and [PERSON] et al. [128], require a scalable infrastructure and spatial-temporal analysis, often employing unsupervised methods, such as k-means or eco-acoustic indexes. These approaches emphasise the importance of context and long-term monitoring rather than just event-based classification. Another interesting differentiation is in the level of hardware integration. Some approaches, such as that of [PERSON] et al. [129], integrate acoustic modules into physical infrastructure (wheel rims), while others, like the crowdsensing system from [PERSON] et al., leverage mobile devices and user participation. This contrast highlights two strategies: passive integration into the environment versus active human involvement, both with their pros and cons. Finally, many works touch on future scalability and integration with other systems (for example, VANET, innovative city platforms, and healthcare monitoring). Yet, none of them propose a unified, multimodal acoustic platform that could operate across domains--an area in which our work provides an integrative vision. The reviewed works demonstrate that acoustic monitoring is rapidly evolving, with robust, efficient, and domain-specific solutions. However, our article's novelty lies in offering a cross-sector perspective, outlining how AI, the IoT, and acoustics converge to form intelligent, scalable systems. This conceptual synthesis, absent in the surveyed literature, marks our contribution as both timely and unique. To better understand the diversity of approaches in intelligent acoustic monitoring, Table 1 summarises selected publications discussed in this article. It contains a comparison of the main features, including application, type of analysed acoustic data, methods of feature extraction and classification used, achieved accuracy, and the most important innovations of each work. Despite the standard core processing of audio signals, we can observe that the approaches are strongly differentiated depending on the application context. The highest accuracies (above 99%) were achieved in biomedical applications and UAV damage detection, where the acoustic environment is relatively controlled. In turn, works carried out in an urban environment (for example, crowdsensing or noise mapping) focused not on classification but on spatial-temporal analysis and supporting decision-makers by interpreting large datasets.