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Supporting Music Education through Visualizations of MIDI Recordings

Frank Heyen, Michael Sedlmair

Abstract

Musicians mostly have to rely on their ears when they want to analyze what they play, for example to detect errors. Since hearing is sequential, it is not possible to quickly grasp an overview over one or multiple recordings of a whole piece of music at once. We therefore propose various visualizations that allow analyzing errors and stylistic variance. Our current approach focuses on rhythm and uses MIDI data for simplicity.

Supporting Music Education through Visualizations of MIDI Recordings

Abstract

Musicians mostly have to rely on their ears when they want to analyze what they play, for example to detect errors. Since hearing is sequential, it is not possible to quickly grasp an overview over one or multiple recordings of a whole piece of music at once. We therefore propose various visualizations that allow analyzing errors and stylistic variance. Our current approach focuses on rhythm and uses MIDI data for simplicity.
Paper Structure (7 sections, 5 figures)

This paper contains 7 sections, 5 figures.

Figures (5)

  • Figure 1: A simplified, notation-based representation of the ground truth drum pattern. This serves as basis for most of our visualizations.
  • Figure 2: Recorded notes of multiple recordings (colored) drawn on top of the ground truth notes. Colors indicate missing, surplus, and correct notes.
  • Figure 3: To reduce clutter, this visualization does not draw single notes but instead uses a density estimation area chart to show the temporal distribution of recorded notes.
  • Figure 4: Drawing only the ground truth, colored by the average error over all recordings, creates a clear image that provides insight into where the problematic parts are.
  • Figure 5: A time-aggregated view shows the distribution of time differences between played and matched correct notes for each drum kit component (here BD = bass drum, SN = snare).