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 am directing 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


Journal Articles, Conference Papers, Datasets

Exploring the foundations of tonality: statistical cognitive modeling of modes in the history of Western classical music

Tonality is one of the most central theoretical concepts for the analysis of Western classical music. This study presents a novel approach for the study of its historical development, exploring in particular the concept of mode. Based on a large dataset of approximately 13,000 musical pieces in MIDI format, we present two models to infer both the number and characteristics of modes of different historical periods from first principles: a geometric model of modes as clusters of musical pieces in a non-Euclidean space, and a cognitively plausible Bayesian model of modes as Dirichlet distributions. We use the geometric model to determine the optimal number of modes for five historical epochs via unsupervised learning and apply the probabilistic model to infer the characteristics of the modes. Our results show that the inference of four modes is most plausible in the Renaissance, that two modes–corresponding to major and minor–are most appropriate in the Baroque and Classical eras, whereas no clear separation into distinct modes is found for the 19th century.

The Tonal Diffusion Model

Pitch-class distributions are of central relevance in music information retrieval, computational musicology and various other fields, such as music perception and cognition. However, despite their structure being closely related to the cognitively and musically relevant properties of a piece, many existing approaches treat pitch-class distributions as fixed templates. In this paper, we introduce the Tonal Diffusion Model, which provides a more structured and interpretable statistical model of pitch-class distributions by incorporating geometric and algebraic structures known from music theory as well as insights from music cognition. Our model explains the pitch-class distributions of musical pieces by assuming tones to be generated through a latent cognitive process on the Tonnetz, a well-established representation for harmonic relations. Specifically, we assume that all tones in a piece are generated by taking a sequence of interval steps on the Tonnetz starting from a unique tonal origin. We provide a description in terms of a Bayesian generative model and show how the latent variables and parameters can be efficiently inferred. The model is quantitatively evaluated on a corpus of 248 pieces from the Baroque, Classical, and Romantic era and describes the empirical pitch-class distributions more accurately than conventional template-based models. On three concrete musical examples, we demonstrate that our model captures relevant harmonic characteristics of the pieces in a compact and interpretable way, also reflecting stylistic aspects of the respective epoch.

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 Brazil in the second half of the 19th century. We evaluate its description in the recent and comprehensive theoretical work A estrutura do Choro (Almada, 2006) by analyzing a set of representative pieces from the Choro Songbook (Chediak, 2009, 2011a,b), a central reference for this genre. We digitized this resource by transcribing the chord symbols and formal structure of all 295 pieces, and publish it as the freely available Choro Songbook Corpus. Our approach uncovers central stylistic traits of this musical idiom on empirical grounds. It thus advances data-driven musical style analysis by studying both harmony and form in a musical genre that lies outside the traditional canon.


Past and Upcoming

Discovering the line of fifths in a large historical corpus

An increasing number of corpus studies relies on pitch-class distributions in order to infer characteristics of musical pieces under a …

The importance of modeling in computational musicology

In recent years, the increasing interest of music theorists in algorithmic analysis as well as the growing amount of musical corpora …

Tonality: Perspectives of historical musicology and corpus studies

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 …

Workshop: Data-Driven Music History

Traditionally, there has been a strict separation between the humanities and the sciences, encompassing qualitative-hermeneutic and …