I am a postdoctoral researcher in the Digital and Cognitive Musicology Lab (DCML) at École Polytechnique Fédérale de Lausanne (EPFL, 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 (with François Bavaud and Coline Métrailler, Université de Lausanne), supported by the EPFL-UNIL funding scheme CROSS - Collaborative Research on Science and Society.


  • Computational Musicology
  • Music Theory
  • Music Cognition
  • Digital Humanities


  • 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



Postdoctoral Researcher

École Polytechnique Fédérale de Lausanne

Feb 2020 – Present Lausanne, Switzerland
2020 - today: Distant Listening - The Development of Harmony over Three Centuries (1700–2000)
2021: Digitizing the Dualism Debate: A Case Study in the Computational Analysis of Historical Music Sources

Doctoral Assistant

École Polytechnique Fédérale de Lausanne

Sep 2017 – Feb 2020 Lausanne, Switzerland

Doctoral Assistant

Technische Universität Dresden

Jan 2015 – Aug 2017 Dresden, Germany


Journal Articles, Conference Papers, Datasets

The Tonal Diffusion Model

Pitch-class distributions are of central relevance in music information retrieval, computational musicology and various other fields, …

Harmony and Form in Brazilian Choro: A Corpus-Driven Approach to Musical Style Analysis

This corpus study constitutes the first quantitative style analysis of Choro, a primarily instrumental music genre that emerged in …


The TP3C (‘tonal pitch-class counts corpus’) is a dataset of tonal pitch-class counts of Western classical music pieces …

Transitions of Tonality: A Model-Based Corpus Study

Tonality has been the cornerstone of Western music-theoretical discourse for centuries. This study addresses the subject, using …

The pleasantness of sensory dissonance is mediated by musical style and expertise

Western musical styles use a large variety of chords and vertical sonorities. Based on objective acoustical properties, chords can be …


Past and Upcoming

Workshop: Analyzing musical pieces on the Tonnetz using the pitchplots Python library

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 and demonstrates how it can be used to address the above issues. It allows to analyze digital encodings of musical pieces by means of graphical representations such as the circle of fifths and the Tonnetz, each of which can be understood as a “tonal fingerprint”. They can be used to reveal particularly interesting tonal aspects within a piece, e.g. its overall tonality (diatonic vs. chromatic); octatonic, hexatonic and other extended tonal relations; or the centrality of certain sonorities, as well as visual comparisons between, e.g. diatonic vs. chromatic pieces. The library is thus not only a useful tool for music analysts but also a pedagogical resource for students, fostering a deeper understanding of computational approaches to music analysis in general as well as tonal relations on the Tonnetz in particular. The workshop is split into a short presentation, a hands-on part of guided exercises, and a final discussion about the limitations and benefits of computational music analysis. Participants are not required to have any prior programming experience and do not need to install any software before the workshop except an up-to-date internet browser.

Workshop: Data-Driven Music History

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.

Computational Musicology and the Digital Humanities: Problems, Practices, and Prospects