Time-reversed Impact Dataset

Touch-screens are the basic and convenient human-computer interface. They are extensively used in digital musical applications, where a complex action-perception loop is involved. Therefore, it is crucial to establish a rich vibrotacticle feedback to improve the quality of the user's interaction. This paper explores the capacity of Generative Adversarial Networks (GANs) to generate time-reversed signals that can achieve localized vibrotactile feedback on a rigid surface.         We adopt the Generative Adversarial Networks (GANs) framework for its indicate and flexible training capacity. The experimental setup for data generation and verification consists of an aluminum plate with a piezoelectric actuator bonded to its surface and a compact laser vibrometer. The given signals are sent into the experimental setup and a vibration scan is carried out. A localized peak generated with a signal synthesized by the trained GAN model is observed and studied. Later, different metrics are proposed to evaluate the quality of the generated samples and the obtained localized peak. Finally,  the feasibility of this approach to generate localized vibrations in the range of 200 - 300 Hz for touch-screen applications is verified.