I am a postdoctoral researcher in the Digital and Cognitive Musicology Lab (DCML) at École Polytechnique Fédérale de Lausanne (Switzerland). Working with large symbolic datasets of musical scores and harmonic annotations, I am primarily interested in Computational Music Analysis, Music Theory, Music Cognition, and their mutual relationship.
Currently, I am working for the project Distant Listening: The Development of Harmony over Three Centuries (1700–2000), funded by the Swiss National Science Foundation (PI: Martin Rohrmeier), that aims at providing a large-scale corpus-based account of the historical development of harmony in Western tonal music.
In 2021, I will lead the project Digitizing the Dualism Debate: A Case Study in the Computational Analysis of Historical Music Sources (Co-PI: François Bavaud, UNIL), supported by the EPFL-UNIL funding scheme CROSS - Collaborative Research on Science and Society.
PhD in Digital Humanities, 2019
École Polytechnique Fédérale de Lausanne, Lausanne Switzerland
Staatsexamen Lehramt für Gymnasien und Gesamtschulen (Mathematik, Musik, Erziehungswissenschaft), 2016
Universität zu Köln, Germany
MA in Musicology, 2012
Hochschule für Musik und Tanz, Köln, Germany
Journal Articles, Conference Papers, Datasets
Past and Upcoming
A central debate of 19th-century music theory concerns graphical depictions of tonal relations, commonly called the Tonnetz. More recently, Neo-Riemannian theory (NRT) has utilized it to analyze triadic progressions and minimal voice-leading in the harmonic idioms of 19th-century composers. While NRT elucidates important aspects, it has several limitations. First, it relies on a fundamentally triadic texture, which necessitates a harmonic reduction that lies outside the scope of NRT. Second, drawing Tonnetz diagrams can be tedious and time-consuming. Third, these analyses usually show the presence or absence of triads but not the relative frequencies. Finally, they are often created only for short excerpts and rarely for entire pieces.
This workshop introduces the free and open-source Python library
Traditionally, there has been a strict separation between the humanities and the sciences, encompassing qualitative-hermeneutic and quantitative-empirical methodologies, respectively. This fundamental divide is being challenged by the advent of the recent field of Digital Humanities that addresses, for instance, inherently historical questions with quantitative methods, fueled by the creation of ever larger and more appropriate datasets as well as the development of novel methods and tools. This situation is mirrored within the field of musicology, commonly divided into historical and systematic research agendas, where the emerging subdiscipline of musical corpus studies aims at bridging the methodological gap.
This workshop presents a case study in empirical music history. It first introduces some epistemological issues and then presents a hands-on exercise. Finally, it invites critical discussion about the implications and relevance of the results for other subfields such as music psychology. In doing so, the workshop simulates (nearly) the entire life cycle of a research project, from an initial idea via selecting appropriate operationalisations and measures up to choosing suitable visualisations to communicate the results, e.g. in a research article or a blog post. At each point, participants will be invited to critically reflect the decisions taken. Along the way, more general methods for data analysis (e.g. data transformation, clustering, dimensionality reduction, and plotting) will be introduced. This is expected to benefit participants in a vast number of future projects.
We will use Python as a programming language within the framework of an interactive coding environment. The participants are not expected to have any prior experience with either, and do not need to install any software on their laptops. A web browser and a stable internet connection are the only prerequisites.