## 4 Applications of Intelligent Acoustic Monitoring
### Industry and Predictive Maintenance  
This subsection shows the detection of faults in machines and industrial infrastructure based on sound and vibration analysis, in the context of machine and rotor diagnostics [121; 122; 123; 124; 125; 126; 127]. In [121], [PERSON] et al. present a fault diagnosis system for rotating machines, such as drills, based on acoustic emission (AE) analysis and edge machine learning (ML). Their approach enables the real-time detection and classification of bearing, gear, and fan faults without transmitting data to the cloud, thereby increasing security, reducing latency, and conserving bandwidth. The system was implemented on a low-cost, low-power STM32F407 microcontroller, where all stages--from data acquisition, filtering, and feature extraction to classification--are performed locally. Using an analysis of variance (ANOVA) for feature selection and a decision tree classifier, the system achieved a high accuracy of 96.1%, outperforming many previous methods, especially in terms of performance on limited hardware. The authors demonstrate that edge ML can be an effective and practical solution for industrial machine health monitoring, and due to its low computational requirements, it is well suited for implementation in real-world conditions. In [122], [PERSON] et al. demonstrated that deep learning networks previously trained to recognise sounds and music could be effectively applied to machine fault diagnostics, particularly for detecting bearing damage. For this purpose, they used the YAMNet model, which was created to analyse sounds from video recordings (for example, YouTube). They applied a transfer learning approach, which allowed them to reuse the learned network features to analyse machine vibration spectrograms. The studies showed that even with a limited quantity of training data, it was possible to achieve very high classification efficiency (even 100% in some datasets), and the appropriate use of dropout layers allowed them to reduce the problem of overfitting in the case of uneven class distribution. The results confirmed that transferring knowledge from sound recognition to machine diagnostics was not only possible but also highly practical, especially in scenarios where access to data is limited. With this approach, the authors planned further research on the impact of dataset size, noise, and hyperparameter selection on diagnostic efficiency. In [123], [PERSON] et al. presented a deep learning-based anomaly detection system for industrial machine sounds that worked effectively even in high ambient noise conditions, one of the main challenges in less automated industrial plants. The main innovation is the use of a GAN to reconstruct sound signals and identify deviations from standard operating patterns, taking into account preprocessing using the Kalman filter and the L2 regularised loss function. The studies were conducted on the MIMI dataset. They demonstrated that the proposed solution outperformed other methods (OC-SVM, classic autoencoder) in terms of fault detection accuracy (AUC), particularly in cases involving noisy pump data. Based on the analysis of Log-Mel spectrograms and optimised for operation in industrial conditions, the system could support predictive maintenance in the spirit of Industry 4.0. The authors planned to further develop the method by designing more noise-tolerant features and loss functions, as well as implementing deep convolutional networks for shorter audio data sections. This was expected to improve the system's performance in real production conditions. In [124], [PERSON] et al. developed a prototype diagnostic system for detecting and locating damaged rotor blades in multi-rotor drones (UAVs) by analysing acoustic signals recorded by an onboard microphone array. Their solution used a data-driven approach using artificial neural networks and MFCC-based features. This enabled the precise detection of rotor damage, including tip cracks and blade edge damage, and the identification of the failed rotor.