In recent years, touchscreens have become ubiquitous, and projected capacitance (p-cap) has arisen as the dominant touch-sensing technology. Though powerful, p-cap has some characteristics that make it less suited for certain applications. Active acoustic sensing (AAS) systems work by sensing the vibration response of an elastic surface to a system-generated excitation and associating specific response characteristics with touch locations on the surface. These systems have the potential to offer advantages over p-cap in terms of scalability, cost, and performance in harsh environments. AAS systems may also offer the ability to sense touch pressure as an additional input parameter. We developed an empirical model of surface vibrational response as a function of the location of an applied force and then used this model to inform the signal processing and machine learning approaches employed in a prototype AAS system. In this presentation, we provide results from our empirical model as well as details of the prototype system, including its construction and performance.