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Acoustic scene analysis has proven an invaluable tool in diverse fields ranging from biology to defense and security. Wireless sensor networks present an attractive means of implementing an acoustic monitoring system due to their low cost and ability to be easily deployed in a wide range of areas. In this paper acoustic features are extracted at the sensor level, and then transmitted to the base station where acoustic events are segmented using a dynamic Bayseian network. Segmented events are then indexed with the time and location where they occurred, allowing users to link events in terms of time, place, and acoustic characteristics. Our experiments show that a feature set that allows for general characterization of diverse sound environments can be extracted at the sensor level, while an illustrative example shows the segmentation algorithm detecting footsteps in low SNR conditions.