Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applica- tions involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computa- tion and communication operations in the embedded sensor nodes becomes critical. Digital signal processing (DSP) applications typi- cally require intensive data processing operations. They are difficult to apply directly in resource-limited WSNs because their operational complexity can strongly influence the network lifetime. In this paper, we present a design methodology for modeling and implementing DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including acoustic sensing and data processing; derives formulations of energy-driven partitioning for distribut- ing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. A case study involving a speech recognition system demonstrates the capabilities of our proposed method- ology.