In recent years, wireless sensor networks (WSN) have shown success in distributed real-time signal processing systems. In collaborative signal processing environments, each sensor is responsible for extracting pertinent information from the surrounding environment and transmitting it to other sensors and/or to the main processing station. Often times, the sensors operate under a number of constraints, such as limited processing power and low bandwidth. In this paper we propose a collaborative signal processing framework that is implemented in an acoustic monitoring scenario. A low-complexity voice activity detector and a gender classifier are implemented on the Crossbow sensor motes. A series of experiments are presented that characterize the performance of the algorithms under varying SNR conditions and in different environments.