## 3 Technologies Supporting Intelligent Acoustic Monitoring
### Artificial Intelligence in Acoustic Analysis  
The computer science discipline of AI develops systems that execute tasks that need human intelligence. The field encompasses a range of techniques, from basic search algorithms to sophisticated machine learning models and neural networks. The application of AI technology extends across various domains, including data analysis, process automation, robotics, medical applications, and cybersecurity. Artificial intelligence enables the analysis of sounds through acoustic processing, which surpasses the capabilities of traditional methods. AI enables sound recording and storage capabilities and real-time analysis for meaning detection and pattern classification, leading to immediate conclusions [4]. We can use AI in many areas, like medicine [32; 33; 34], critical systems [35], security [36; 37; 38; 39], or logistics [40; 41; 42].  
AI plays a key role in sound analysis, especially in Natural Language Processing (NLP) [43; 44; 45], sound classification [46; 47; 48], and acoustic pattern recognition [49; 50; 51]. The artificial intelligence branch of NLP enables machines to understand and process human language in both spoken and written forms. NLP enables speech recognition, language translation, sentiment analysis, and transcription of recordings in sound analysis applications. Voice assistants, voice control systems, and conversation monitoring systems use this technology to detect undesirable content. Sound classification is crucial because AI systems can identify various types of sound signals, including background noise, machine sounds, alarm sounds, and human speech. The technology enables industrial system fault detection through automation, supporting the recording of anomaly evaluation and music and voice identification. AI technology detects acoustic patterns to achieve three main objectives: medical diagnosis of lung diseases using cough analysis, security applications with speaker recognition, and environmental studies of animal vocalisations for ecological research.  
Sound analysis relies on multiple AI algorithms that effectively process and understand acoustic signals. The Convolutional Neural Network (CNN) model [52; 53; 54] is a widely used solution that has demonstrated outstanding performance in image and sound spectrogram analysis. The analysis of sequential signals with the detection of temporal dependencies depends on recurrent neural networks (RNNs) [55; 56; 57] and their advanced version, Long Short-Term Memory (LSTM) [58; 59; 60]. Speech recognition and acoustic analysis have been revolutionised by Transformer-based models, including Whisper from OpenAI [61] and Wav2 Vec2 from Meta AI [62], which deliver highly accurate speech transcription and interpretation results. Sound classification and recognition systems use supervised and unsupervised learning models, including Support Vector Machines (SVMs) [63; 64; 65] and Gaussian Mixture Models (GMMs) [66; 67; 68] for sound source segmentation and recognition. Implementing these algorithms enables both automatic sound analysis and real-time processing of large recordings, as well as speaker identification and anomaly detection.  
Figure 2: Layered architecture of an intelligent acoustic IoT system. Each layer includes representative components for sensing, data transmission, edge intelligence, and cloud-based processing.