The need to go beyond standardized approaches to characterizing urban noise environments, and to offer appropriate technical insights for decision-making, is a consensus in the urban noise research community. However, the diversification of approaches for characterizing urban noise environments is still hampered by the lack of open and reproducible modeling frameworks. This presentation examines recent advancements in characterizing urban sound environments, emphasizing a global, open-source framework. Utilizing tools such as the environmental noise prediction tool NoiseModelling, the smartphone noise measurement application NoiseCapture, and low-cost noise sensors, the discussion covers soundscape- oriented multi-source modeling, data assimilation, and sound recognition for advanced noise impact assessment. Additionally, modeling frameworks incorporating mobility modeling at various spatial scales to better manage urban noise will be showcased. The presentation concludes with examples of advanced soundscape description and representation and citizen science approaches. These new ways of characterizing urban sound environments will be discussed in light of the improvement of impact assessment and decision-making they can facilitate.