Sound source detection and recognition using acoustic sensors are increasingly used to monitor and analyze the urban environment as they enhance soundscape characterization and facilitate the comparison between simulated and measured noise maps using methods such as Artificial Neural Networks or Non-negative Matrix Factorization. However, the community lacks corpuses of sound scenes whose acoustic properties of each source present within the scene are precisely known. In this study, a set of 40 sound scenes typical of urban sound mixtures is created in three steps: (i) real sound scenes are listened and annotated in terms of events type, (ii) artificial sound scenes are created based on the concatenation of recorded individual sounds, whose intensity and duration are controlled to build scenes that are as close as possible to the real ones, (iii) a test is carried out to validate the level of their perceptual realism of those crafted scenes. Such corpus could be then use by communities interested in the study of the urban sound environments.