In this paper, we bridge the gap between visualization and musicology by focusing on rhythm analysis tasks, which are tedious due to the complex visual encoding of the well-established Common Music Notation (CMN).

Instead of replacing the CMN, we augment sheet music with rhythmic fingerprints to mitigate the complexity originating from the simultaneous encoding of musical features. The proposed visual design exploits music theory concepts such as the rhythm tree to facilitate the understanding of rhythmic information. Juxtaposing sheet music and the rhythmic fingerprints maintains the connection to the familiar representation.

To investigate the usefulness of the rhythmic fingerprint design for identifying and comparing rhythmic patterns, we conducted a controlled user study with four experts and four novices. The results show that the rhythmic fingerprints enable novice users to recognize rhythmic patterns that only experts can identify using non-augmented sheet music.

@article{fuerst:2020:rhythmicfingerprints,
  author    = {Daniel F{\"{u}}rst and
               Matthias Miller and
               Daniel A. Keim and
               Alexandra Bonnici and
               Hanna Sch{\"{a}}fer and
               Mennatallah El{-}Assady},
  title     = {{Augmenting Sheet Music with Rhythmic Fingerprints}},
  journal   = {IEEE Workshop on Visualization for the Digital Humanities (VIS4DH)},
  year      = {2020},
  doi       = {10.1109/VIS4DH51463.2020.00007}
}