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International audience; The visualization of sounds facilitates their identification and classification. However, in the case of audio recording websites, the access to a sound is usually based on the metadata of the sounds, i.e.sources and recording conditions. As sonic environments, or soundscapes, are mostly composed of multiples sources, their compact description is an issue that makes difficult the choice of an item in a sound corpus. The time-component matrix chart, which is abbreviated as TM-chart, has been proposed recently as a tool to describe and compare sonic environments. However their process of creation is based on a subjective annotation that makes their creation time-consuming. In this paper, we present a new method for urban soundscape corpus visualization. In the context of the CIESS project, we propose amochart: an extension of the TM-chart that is based on sound detection algorithms. We describe three original algorithms that allow the detection of alarms, footsteps, and motors. Samocharts can be computed from the results of these algorithms. This process is applied to a concrete case study: 20 urban recordings of 5 minutes each, from different situations (places and time). An application case shows that Samocharts allow an identification of different situations. Finally, the whole method provides a low-cost tool for soundscape visualization that can easily be applied to the management and use of a sound corpus.