I'm a PhD candidate at the University of Stuttgart, Germany, whithin the awesome VISVAR research group.

My thesis revolves around supporting people who want to learn a musical instrument, by giving them visual insight into what they play. My VIS'20 poster explains the idea in more detail.

This website shows off some of the stuff I did for work, research, and fun (they overlap sometimes). See also my Observable page, where smaller projects live. I'm a strong believer in open science, open source, and web technoloqy, feel free to re-use anything as long as you cite and/or link the original :).

ClaVis: An Interactive Visual Comparison System for Classifiers

AVI 2020 Frank Heyen, Tanja Munz, Michael Neumann, Daniel Ortega, Ngoc Thang Vu, Daniel Weiskopf, Michael Sedlmair

PDF ACM code & more

We propose ClaVis, a visual analytics system for comparative analysis of classification models. ClaVis allows users to visually compare the performance and behavior of tens to hundreds of classifiers trained with different hyperparameter configurations. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that reveals correlations between parameters and scores, and a training history chart. We demonstrate the effectivity of our approach in multiple case studies for training classification models in the domain of natural language processing.

Supporting Music Education through Visualizations of MIDI Recordings

IEEE VIS 2020 Frank Heyen, Michael Sedlmair

poster paper PDF

Musicians mostly have to rely on their ears when they want to analyze what they play, for example to detect errors. Since hearing is sequential, it is not possible to quickly grasp an overview over one or multiple recordings of a whole piece of music at once. We therefore propose various visualizations that allow analyzing errors and stylistic variance. Our current approach focuses on rhythm and uses MIDI data for simplicity.

Illegible Semantics: Exploring the Design Space of Metal Logos

IEEE alt.VIS 2021 Gerrit J. Rijken, Rene Cutura, Frank Heyen, Michael Sedlmair, Michael Correll, Jason Dykes, Noeska Smit

arXiv paper PDF supplemental website

The logos of metal bands can be by turns gaudy, uncouth, or nearly illegible. Yet, these logos work: they communicate sophisticated notions of genre and emotional affect. In this paper we use the design considerations of metal logos to explore the space of "illegible semantics": the ways that text can communicate information at the cost of readability, which is not always the most important objective. In this work, drawing on formative visualization theory, professional design expertise, and empirical assessments of a corpus of metal band logos, we describe a design space of metal logos and present a tool through which logo characteristics can be explored through visualization. We investigate ways in which logo designers imbue their text with meaning and consider opportunities and implications for visualization more widely.

ISMIR 2021 (poster) Simeon Rau, Frank Heyen, Michael Sedlmair

poster paper PDF video

We propose a visual approach for AI-assisted music composition, where the user interactively generates, selects, and adapts short melodies. Based on an entered start melody, we automatically generate multiple continuation samples. Repeating this step and in turn generating continuations for these samples results in a tree or graph of melodies. We visualize this structure with two visualizations, where nodes display the piano roll of the corresponding sample. By interacting with these visualizations, the user can quickly listen to, choose, and adapt melodies, to iteratively create a composition. A third visualization provides an overview over larger numbers of samples, allowing for insights into the AI's predictions and the sample space.

More to come!