Speaker
Description
Hospital noise severely impacts patient recovery and staff well-being. Traditional metrics fail to capture true perceived annoyance, while accurate psychoacoustic models require continuous audio recordings, violating clinical privacy constraints. Conversely, current intrusiveness standards (UNI/TS 11844) use privacy-compliant sound level meters but only evaluate energetic emergence, ignoring the tonal and fluctuating characteristics of sound. This study compares the psychoacoustic annoyance and intrusiveness of isolated noise events (alarms, speech, equipment) in an Italian hospital ward. To bridge the gap between existing models, a data-driven methodology is proposed. The aim is to introduce a weighted Intrusiveness Index. The model extracts temporal variance and spectral prominence from the acoustic time-history, generating objective penalties that simulate fluctuation and tonality. This approach aims to approximate psychoacoustic annoyance solely from sound-level data. It provides researchers and designers with a robust, scalable, and privacy-compliant tool to assess acoustic comfort in healthcare environments without compromising patient confidentiality.