Browsing by Author "Miller, Matthias"

Now showing 1 - 4 of 4
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    Augmenting Digital Sheet Music through Visual Analytics
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Miller, Matthias; Fürst, Daniel; Hauptmann, Hanna; Keim, Daniel A.; El‐Assady, Mennatallah; Hauser, Helwig and Alliez, Pierre
    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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    CorpusVis: Visual Analysis of Digital Sheet Music Collections
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Miller, Matthias; Rauscher, Julius; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair-analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.
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    Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Schetinger, Victor; Bartolomeo, Sara Di; El-Assady, Mennatallah; McNutt, Andrew; Miller, Matthias; Passos, João Paulo Apolinário; Adams, Jane L.; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains-from logo design to digital painting to photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied in visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization.We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Through this work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.
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    Visual Exploration of Preference-based Routes in Ski Resorts
    (The Eurographics Association, 2022) Rauscher, Julius; Miller, Matthias; Keim, Daniel A.; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Ski resorts exhibit a variety of available pistes and lifts, to which every skier has intrinsic preferences. While novices tend to favor easy pistes, experts might opt for more advanced pistes. In large resorts, the vast possibilities render manual, optimized routing according to specific piste and lift preferences extremely tedious. So far, existing visualizations of ski resorts lack these routing capabilities.We present a visual analytics interface that allows the user to find an optimal route between arbitrary locations in a ski resort according to individual personal preferences. Furthermore, we encode steepness information along the pistes to expose segments that deviate from the difficulty classification and thus are incompatible with the given user preferences.