In epidemiological studies focusing on mental health, well-being, and cognitive development, noise exposure is often addressed in a rather imprecise manner. Historically, the lack of affordable and flexible noise monitoring devices necessitated reliance on computational noise models. However, the choice of noise model and its underlying assumptions can lead to significant discrepancies in calculated exposure levels. Three key limitations in current practices can be identified: Façade Noise Levels and Sleep Disturbance: Noise levels are typically calculated for the most exposed façade, neglecting the possibility that a dwelling may have a quieter side where the bedroom—crucial for sleep, an identified mediator for many studied effects—may be located. Furthermore, for assessing sleep disturbance, indoor noise levels are more relevant. Given modern building codes emphasizing energy efficiency, open windows during sleep are no longer standard. Unfortunately, conventional noise models perform poorly in accounting for shielded façades and courtyard acoustics, which can lead to underestimations of exposure in such areas. Simplistic Noise Indicators: The most commonly used indicators, such as façade Lden and Lnight, rely on A-weighted equivalent sound levels, which are straightforward to calculate. While these indicators show high spatial correlation with more sophisticated metrics, they lack the validity needed for nuanced analyses. As a result, researchers often forego more complex calculation models despite their higher accuracy in certain contexts. Limited Source Representation: Noise exposure calculations frequently focus on specific sources (e.g., road traffic) or subsets thereof (e.g., traffic on major roads), excluding other contributors. However, this limitation is partially offset by the fact that specific sources produce characteristic spectro-temporal sound patterns, which can aid in identifying associations with health outcomes. In the Equal-Life project, the aforementioned shortcomings were addressed by integrating direct measurements and introducing conceptually robust noise indicators, alongside methods for their efficient calculation