Creating an audio database from continuous long-term recordings, allows for sounds to not only be linked by the time and place in which they were recorded, but also to sounds with similar acoustic characteristics. Of paramount importance in this application is the accurate segmentation of sound events, enabling realistic navigation of these recordings. We first propose a novel feature set of specific relevance to environmental sounds, and then develop a Bayesian framework for sound segmentation, which fuses dynamics across multiple features. This probabilistic model possesses the ability to account for non-instantaneous sound onsets and absent or delayed responses among individual features, providing flexibility in defining exactly what constitutes a sound event. Example recordings demonstrate the diversity of our feature set, and the utility of our probabilistic segmentation model in extracting sound events from both indoor and outdoor environments.