  Noisy vibrotactile signals transmitted during tactile explorations of an
object provide precious information on the nature of its surface. Linking the
properties of such vibrotactile signals to the way they are interpreted by the
haptic sensory system remains challenging. In this study, we investigated
humans' perception of noisy, stationary vibrations recorded during exploration
of textures and reproduced using a vibrotactile actuator. Since intensity is a
well-established essential perceptual attribute, an intensity equalization was
first conducted, providing a model for its estimation. The equalized stimuli
were further used to identify the most salient spectral features in a second
experiment using dissimilarity estimations between pairs of vibrations. Based
on dimensionally reduced spectral representations, linear models of
dissimilarity prediction showed that the balance between low and high
frequencies was the most important cue. Formal validation of this result was
achieved through a Mushra experiment, where participants assessed the fidelity
of resynthesized vibrations with various distorted frequency balances. These
findings offer valuable insights into human vibrotactile perception and
establish a computational framework for analyzing vibrations as humans do.
Moreover, they pave the way for signal synthesis and compression based on
sparse representations, holding significance for applications involving complex
vibratory feedback.
