Digital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non‐expert users in data analysis and in gaining new knowledge. This survey focuses on this unique link between musicology and visualization. We classify 129 related works according to the visualized data types, and we analyse which visualization techniques were applied for certain research inquiries and to fulfill specific tasks. Next to scientific references, we take commercial music software and public websites into account, that contribute novel concepts of visualizing musicological data. We encounter different aspects of uncertainty as major problems when dealing with musicological data and show how occurring inconsistencies are processed and visually communicated. Drawing from our overview in the field, we identify open challenges for research on the interface of musicology and visualization to be tackled in the future.

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Medieval textuality is characterized by instability in text structure and length that varies according to the text tradition. This instability in the versions, otherwise known as “mouvance”, is characterized by dialectal difference, traces of orality, the modification of wording and even the rewriting and rearrangement of large parts of the text. To help humanities scholars in the exploratory analysis of such complex text collections, the visual analytic system iteal was initially proposed. The system aligns similar phrases on a line-level on the basis of string similarity and word n-grams. We propose an extension of this system that replaces the parameter-based approach with an automatic one using word embeddings thereby adding a semantic component. The benefit of the new visualization system is shown through a comparison of different versions of medieval French texts. Additionally, a domain-expert compared the parameter-based approach with the approach based on word embeddings to outline the similarities and differences in the alignments.

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In response to the COVID-19 pandemic, public spaces such as museums and art galleries are experiencing increased demands to offer virtual online access. While current solutions seek to replace or augment a real visit, online tours often suffer from being too passive and lack in-depth interactivity to keep virtual visitors meaningfully engaged with an exhibition. Museums and art galleries seeking to broaden and engage their audience more deeply should offer intriguing experiences that invite the visitor to explore, to be entertained, and to learn by interacting with the content. We propose a novel virtual museum experience that utilizes multiple visualizations to contextualize a gallery’s digitized artworks with related artworks from large image archives. We make use of the WikiArt data set that includes more than 200,000 images and offers diverse metadata used for comparative visual exploration. In addition, we apply machine learning methods to extract multifaceted information about the objects detected in the images and to compute similarities across them. Visitors of our virtual museum can interactively explore the artworks using different search filters such as artist, style, or object classes detected within an image. The results are displayed through interactive visualizations offering different perspectives on artwork collections, leading to serendipitous discoveries and stimulating new insights. The utility of our concept was confirmed by an informal evaluation with virtual museum visitors.