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.
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
This study focuses on the exploration of the possibilities arising from the application of an NLP word-embedding method (Word2Vec) to a large corpus of musical chord sequences, spanning multiple musical periods. First, we analyse the clustering of the embedded vectors produced by Word2Vec in order to probe its ability to learn common musical patterns. We then implement an LSTM-based neural network which takes these vectors as input with the goal of predicting a chord given its surrounding context in a chord sequence. We use the variability in prediction accuracy to quantify the stylistic differences among various composers in order to detect idiomatic uses of some chords by some composers. The historical breadth of the corpus used allows us to draw some conclusions about broader patterns of changing chord usage across musical periods from Renaissance to Modernity.
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.
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.
Past and Upcoming
Background A central finding in music perception is that listeners’ ratings of the stability of probe tones against the background of a …
Background Fundamental insights on mental representations of musical structure have been gained using the probe-tone paradigm, where …
An increasing number of corpus studies relies on pitch-class distributions in order to infer characteristics of musical pieces under a …
In recent years, the increasing interest of music theorists in algorithmic analysis as well as the growing amount of musical corpora …