Augmenting Sheet Music with Rhythmic Fingerprints

Fürst, Daniel; Miller, Matthias; Keim, Daniel A.; Bonnici, Alexandra; Schäfer, Hanna; El‐Assady, Mennatallah

doi:10.1109/vis4dh51463.2020.00007

Cited by 2 publications

(7 citation statements)

References 37 publications

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“…The technique description builds the foundation for possible analysis applications. We implemented the technique as an exemplary instance ( MusicVis ) by using existing abstract visualization designs that we published in our previous work [FMK*20, MBEA19]. Figure 1 illustrates the different analysis components of MusicVis .…”

Section: Instantiation: Musicvismentioning

confidence: 99%

“…Design rationale — MusicVis integrates interactive highlighting, linking & brushing, and details‐on‐demand to improve the connection of the glyph abstractions to the CMN as required by the technique. In previous work, we have introduced glyph designs for harmony [MBEA19] and rhythm [FMK*20]. Both visual fingerprint designs build on music domain knowledge.…”

Section: Instantiation: Musicvismentioning

confidence: 99%

“…Rhythm — Besides harmony, rhythm is the second fundamental dimension in music. Similarly, to provide separate visual fingerprints for rhythm, we use the visual encoding that we proposed in a previous publication employing the metaphor of a clock [FMK*20]. If the presence of notes and rests collide at a position in the glyph, the rhythmic fingerprint prioritizes the presence of rhythm over its absence (rests), which are more relevant for the perceived rhythm of the music [LGA*11].…”

Section: Instantiation: Musicvismentioning

confidence: 99%

“…We demonstrate the technique's capabilities by presenting an instantiation, called MusicVis ( visual‐musicology.com/sheetmusicvis ). MusicVis uses existing visual designs such as the harmonic and rhythmic fingerprint from our previous work [MBEA19, FMK*20] to display separate musical aspects from simultaneously encoded information. We follow these visual designs to provide visual querying functionality, supporting tailored pattern detection based on the analysts' understanding and domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Augmenting Digital Sheet Music through Visual Analytics

Miller

Fürst

Schäfer

et al. 2022

Computer Graphics Forum

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Music analysis tasks, such as structure identification and modulation detection, are tedious when performed manually due to the complexity of the common music notation (CMN). Fully automated analysis instead misses human intuition about relevance. Existing approaches use abstract data‐driven visualizations to assist music analysis but lack a suitable connection to the CMN. Therefore, music analysts often prefer to remain in their familiar context. Our approach enhances the traditional analysis workflow by complementing CMN with interactive visualization entities as minimally intrusive augmentations. Gradual step‐wise transitions empower analysts to retrace and comprehend the relationship between the CMN and abstract data representations. We leverage glyph‐based visualizations for harmony, rhythm and melody to demonstrate our technique's applicability. Design‐driven visual query filters enable analysts to investigate statistical and semantic patterns on various abstraction levels. We conducted pair analytics sessions with 16 participants of different proficiency levels to gather qualitative feedback about the intuitiveness, traceability and understandability of our approach. The results show that MusicVis supports music analysts in getting new insights about feature characteristics while increasing their engagement and willingness to explore.

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Section: Instantiation: Musicvismentioning

confidence: 99%

Section: Instantiation: Musicvismentioning

confidence: 99%

Section: Instantiation: Musicvismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Augmenting Digital Sheet Music through Visual Analytics

Miller

Fürst

Schäfer

et al. 2022

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…A recent survey on music visualization [11] shows that despite a range of different applications such as visualization of instrument hardware, audio [4], sheet music [5,16,17], artist networks, and listening histories [3], there has still been little work in this domain. One task that has not been addressed much in this area is the analysis and visual comparison [6] of motion and exercise recordings.…”

Section: Related Workmentioning

confidence: 99%