## 3 Technologies Supporting Intelligent Acoustic Monitoring
### Audio Signal Processing Techniques  
The fundamental components of sound analysis in modern AI systems are noise reduction and feature extraction. Combining these methods produces clean and structured signals that can be applied to various speech recognition applications, including security sound detection, factory defect identification, and healthcare breath sound analysis for the diagnosis of lung diseases. Pattern analysis and sound classification rely heavily on artificial intelligence and machine learning. Instantaneous recognition, comprehension, and handling of sound signals by AI algorithms are helpful in online security, healthcare, industry analysis and in capable support systems. Machine learning models can recognise particular sound patterns, classify them based on previously learnt information, and detect unusual signals that could indicate equipment issues, eavesdropping attempts, or other undesirable circumstances. Sound classification converts sound waves into numerical values and identifies various sound types using techniques such as neural networks (CNN, RNN, Transformer), SVMs, or GMMs. AI can differentiate between signals, including speech, music, background noise, and odd noises in industrial settings, provided it is properly processed and analysed [3]. Sound capture techniques are crucial for recording signal quality and subsequent analysis. Classic microphones operate on the principle of membranes reacting to acoustic waves, converting them into electrical signals. Despite their widespread use in various applications, they suffer from limited susceptibility to interference and background noise. Adaptive microphones, unlike classical ones, can dynamically adjust their characteristics to the surrounding conditions, filtering out unwanted sounds and focusing on the sound source. They rely on speech recognition systems, IoT devices, and intelligent acoustic monitoring systems. Another advanced technology is beamforming, which tracks sound sources in microphone arrays. It involves using multiple microphones to selectively amplify the signal from a specific direction, thereby significantly improving the recorded audio quality and reducing background noise. It is a key technology in video conferencing systems, speech recognition in noisy environments, and military and industrial applications. You can capture sound signals in two ways: passive methods, which listen to sounds around us, and active methods, which send out sound waves and study how they bounce back. Active methods, such as sonar or echolocation, are used in object detection, underwater navigation, and intrusion detection systems [69; 70]. The extraction of key sound characteristics and their accurate analysis are made possible by sophisticated audio signal processing techniques. A crucial tool for identifying the harmonic components of sound is the fast Fourier transform (FFT) [71], which transforms a signal from the time domain to the frequency domain. The method is essential for sound recognition systems, disturbance detection, and signal spectrum analysis. Sound classification and speech recognition applications rely on Mel-Frequency Cepstral Coefficients (MFCCs) [72] as a crucial technique. Audio signal processing systems utilise MFCCs as a valuable feature extraction method because it emulates human auditory perception of sounds. The short-time Fourier transform (STFT) [73] in spectrogram analysis displays signal frequency changes over time to detect abnormal sounds, analyse music, and enhance security systems. Advanced sound analysis systems emerge from combining these methods with AI algorithms, which enable pattern recognition, signal sorting, and environmental sound adaptation. Noise reduction is essential for enhancing the quality of sound signals, especially in systems that recognise speech, classify sounds, and process microphone signals in noisy places.