Detecting a Signal Of Interest (SOI) is the first step in many applications of infrasound monitoring. This intuitively simple task is defined as separating out signals from background noise on the basis of the characteristics of observed data; it is, however, deceptively complex. The problem of detecting signals requires multiple processes that are divisible at their highest level into several fundamental tasks. These tasks include (1) defining models for SOIs and noise that properly fit the observations, (2) finding SOIs amongst noise, and (3) estimating parameters of the SOI (e.g., Direction Of Arrival (DOA), Signal-to-Noise Ratio (SNR) and confidence intervals) that can be used for signal characterization. Each of these components involves multiple subcomponents. Here, we explore these three components by examining current infrasound detection algorithms and the assumptions that are made for their operation and exploring and discussing alternative approaches to advance the performance and efficiency of detection operations. This chapter does not address new statistical methods but does offer some insights into the detection problem that may motivate further research.