  The vibrations generated from scratching and tapping on surfaces can be
highly expressive and recognizable, and have therefore been proposed as a
method of natural user interface (NUI). Previous systems require custom sensor
hardware such as contact microphones and have struggled with gesture
classification accuracy.
  We propose a deep learning approach to scratch input. Using smartphones and
tablets laid on tabletops or other similar surfaces, our system achieved a
gesture classification accuracy of 95.8\%, substantially reducing gesture
misclassification from previous works. Further, our system achieved this
performance when tested on a wide variety of surfaces, mobile devices, and in
high noise environments.
  The results indicate high potential for the application of deep learning
techniques to natural user interface (NUI) systems that can readily convert
large unpowered surfaces into a user interface using just a smartphone with no
special-purpose sensors or hardware.
