The aim of this thesis is to develop innovative algorithms to support individuals with auditory impairments, enhancing their autonomy and safety through intelligent embedded systems. Specifically, the system is designed to detect and classify sounds associated with potentially dangerous events, providing timely visual notifications that alert users in real-world situations. These algorithms process auditory input to enrich sensory information and deliver real-time assistance through intuitive feedback mechanisms. The implementation is based on the STMicroelectronics STM32U5-based B-U585I-IOT02A board and the Corellium virtualization environment, which together enable accurate simulation, testing and optimization before deployment. Particular attention has been given to optimizing execution time and memory usage, ensuring real-time hazard detection and efficient resource management on a low-power microcontroller platform.