Augmenting Digital Sheet Music through Visual Analytics

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

doi:10.1111/cgf.14436

Cited by 2 publications

(3 citation statements)

References 92 publications

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“…Eventually, CorpusVis focuses the analysis on a high level. We recently published MusicVis that enables analysts to investigate single compositions at the sheet level [MFH∗22]. We aim at connecting both approaches to allow analysts to filter relevant compositions at a higher level using CorpusVis which can then be further analyzed at the sheet level.…”

Section: Discussionmentioning

confidence: 99%

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CorpusVis: Visual Analysis of Digital Sheet Music Collections

Miller

Julius

Keim

et al. 2022

Computer Graphics Forum

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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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Section: Discussionmentioning

confidence: 99%

“…The sheet view allows viewing the first page of compositions. At the sheet level, analysts can perform harmonic and rhythmic analysis tasks using MusicVis [MFH∗22]. …”

Section: Visual Interactive Analysis Workpacementioning

confidence: 99%

CorpusVis: Visual Analysis of Digital Sheet Music Collections

Miller

Julius

Keim

et al. 2022

Computer Graphics Forum

Self Cite

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“…The extraction of visual design graphics mainly solves not only the aesthetic art problem of visual expression, but also pays more attention to the exploration of social function application and public integration under the development of digital media that needs to be solved by design communication [5]. Applying intelligent visual gene extraction methods to digital design reengineering problems, constructing a multi-system digital visual graphic gene extraction model, and carrying out intelligent digital design reengineering methods [6] are increasingly being paid attention to and researched by experts in the field [7]. Digital visual design reengineering graphic visual gene extraction methods are divided into color system extraction [8], graphic line body extraction [9], texture feature extraction [10] and other methods from the perspective of the type of gene extraction.…”

Section: Introductionmentioning

confidence: 99%

Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm

Ren,

Kim

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability.OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm.METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments.RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms.CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods.

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Augmenting Digital Sheet Music through Visual Analytics

Cited by 2 publications

References 92 publications

CorpusVis: Visual Analysis of Digital Sheet Music Collections

CorpusVis: Visual Analysis of Digital Sheet Music Collections

Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm

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