## 8 Conclusions  
This review aimed to explore how sound, often overlooked or treated as a secondary signal, has come to play a significantly more central role in intelligent systems. We found that across very different application areas, from monitoring road traffic to diagnosing health conditions, sound was not just useful, it was essential. The range of current implementations is already impressive. Some systems detect mechanical faults from just a few seconds of engine noise, while others analyse city soundscapes to identify patterns in biodiversity. But what makes them effective is not only the use of advanced AI techniques. Equally important are practical elements, including the placement and type of sensors, the method of data processing (whether locally or in the cloud), and how the system handles disruptions, noise, or limited power. As we reviewed dozens of studies, a few clear themes emerged. One of them was the steady move toward combining multiple data sources--not just sound, but also video, temperature, and motion. Another was the increasing reliance on decentralised processing, where instead of sending all data to the cloud, they are analysed where they are captured. That shift matters, especially for applications where real-time feedback or data privacy is critical. However, the field still faces significant challenges. Many systems struggle with consistency in noisy or unpredictable environments. Some models perform well in the lab but lose accuracy in the real world. Additionally, there is the question of privacy--continuous listening, even for legitimate reasons, can raise serious concerns when it is carried out in public or semi-private spaces. We also noticed several research gaps. For instance, there was a lack of shared benchmarks across domains, making it difficult to compare results or replicate findings. Very few studies looked at long-term performance or system durability. And in many cases, there was too little discussion of what happens when these systems fail--something we will need to understand better as adoption grows. Looking ahead, the field is likely to evolve in at least two directions. On the one hand, we can expect to see better hardware, including smaller and more sensitive sensors, possibly based on new materials or designs. On the other hand, we will need more intelligent algorithms that can run on the edge, adapt to changing environments, and make decisions even when data are incomplete or messy. Legal and ethical frameworks will also need to keep pace, particularly in terms of transparency and the right to silence. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Application Domain** & **Audio Data Type** & **Feature Extraction** & **Accuracy** & **Remarks/Innovations** \\ \hline
[126] & UAV motor fault detection & Onboard motor sounds (4 UAV types) & MFCC + SVM & 90.5–100\% & Lightweight method for embedded use; tested on helicopter–quadcopter \\ \hline
[127] & Vehicle mechanical failure & In-vehicle acoustic + vibration & MFCC + Kalman filter & Not quantified & Device + algorithm; early fault detection using in-cabin sensors \\ \hline
[119] & Voice disorder detection & Sustained vowels + running speech & LPC + spectral energy & 99.75–99.94\% & Real-world deployable system for daily voice monitoring \\ \hline
[118] & Urban noise crowdsensing & Mobile phones at microlocations & FFT + big data analytics & – & Open-source system; dynamic microlocation selection, decision support \\ \hline
[128] & Urban sound mapping & Wireless acoustic sensors (3 years) & SPL stats (L\({}_{\text{day}}\), L\({}_{\text{veveing}}\), etc.) & – & K-means clustering; discovered new acoustic zones in Barcelona \\ \hline
[129] & Road surface detection & Microphone + ultrasonic module & MFCC + MLP/SVM & 98.98\% (MLP) & Wheel-rim-integrated device; identifies road texture and crack depth \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative summary of selected works in intelligent acoustic monitoring.