Augmenting Digital Sheet Music through Visual Analytics

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MILLER, Matthias, Daniel FÜRST, Hanna HAUPTMANN, Daniel A. KEIM, Mennatallah EL-ASSADY, 2022. Augmenting Digital Sheet Music through Visual Analytics. In: Computer Graphics Forum. Wiley. 41(1), pp. 301-316. ISSN 0167-7055. eISSN 1467-8659. Available under: doi: 10.1111/cgf.14436

@article{Miller2022-02Augme-56142, title={Augmenting Digital Sheet Music through Visual Analytics}, year={2022}, doi={10.1111/cgf.14436}, number={1}, volume={41}, issn={0167-7055}, journal={Computer Graphics Forum}, pages={301--316}, author={Miller, Matthias and Fürst, Daniel and Hauptmann, Hanna and Keim, Daniel A. and El-Assady, Mennatallah} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/56142"> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:language>eng</dc:language> <dcterms:issued>2022-02</dcterms:issued> <dc:contributor>Fürst, Daniel</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:creator>Miller, Matthias</dc:creator> <dc:contributor>Hauptmann, Hanna</dc:contributor> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/56142/1/Miller_2-134k2c5p2dlur3.pdf"/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:creator>El-Assady, Mennatallah</dc:creator> <dc:rights>Attribution-NonCommercial 4.0 International</dc:rights> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-01-17T09:41:02Z</dc:date> <dc:creator>Fürst, Daniel</dc:creator> <dc:contributor>Miller, Matthias</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc/4.0/"/> <dcterms:abstract xml:lang="eng">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.</dcterms:abstract> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/56142"/> <dc:contributor>El-Assady, Mennatallah</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/56142/1/Miller_2-134k2c5p2dlur3.pdf"/> <dcterms:title>Augmenting Digital Sheet Music through Visual Analytics</dcterms:title> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:creator>Keim, Daniel A.</dc:creator> <dc:creator>Hauptmann, Hanna</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-01-17T09:41:02Z</dcterms:available> </rdf:Description> </rdf:RDF>

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