Johannes Hentschel

Biographie

Avant de commencer son doctorat à l'École Polytechnique Fédérale de Lausanne (EPFL), Johannes Hentschel a étudié pédagogie de musique, théorie de musique et français à Fribourg-en-Brisgau, Lübeck et Helsinki. Accordéoniste, chanteur et chef de chorale diplômé, il est avant tout maître de conférences en théorie de musique. Depuis 2018, il poursuit des études doctorales d'Humanités Digitales à l'EPFL. Sous la direction du professeur Martin Rohrmeier au sein du Digital and Cognitive Musicology Lab (DCML) Johannes prépare sa thèse sur les changements diachroniques de styles musicaux, approfondissant ses connaissances dans la construction de corpus et l'organisation de métadonnées.

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https://people.epfl.ch/johannes.hentschel?lang=fr

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The diachronic development of Debussy’s musical style: a corpus study with Discrete Fourier Transform

Gabriele Cecchetti, Johannes Hentschel, Sabrina Laneve, Martin Alois Rohrmeier, Ludovica Schaerf

Claude Debussy’s personal style is typically characterised as a departure from earlier diatonic tonality, including a greater variety of pitch-class materials organised in fragmented yet coherent compositions. Exploiting the music-theoretical interpretability of Discrete Fourier Transforms over pitch-class distributions, we performed a corpus study over Debussy’s solo- piano works in order to investigate the diachronic development of such stylistic features across the composer’s lifespan. We propose quantitative heuristics for the prevalence of different pitch-class prototypes, the fragmentation of a piece across different prototypes, as well as some aspect of the overall coherence of a piece. We found strong evidence for a decrease of diatonicity in favour of octatonicity, as well as for an increase of fragmentation accompanied by non-decreasing coherence. These results contribute to the understanding of the historical development of extended-tonal harmony, while representing a fertile testing ground for the interaction of computational corpus-based methods with traditional music analytical approaches.

2023

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The creation and curation of labeled datasets can be an arduous, expensive, and time-consuming task. We introduce a workflow paradigm for remote consensus-building between expert annotators, while considerably reducing the associated administrative overhead through automation. Most music annotation tasks rely heavily on human interpretation and therefore defy the concept of an objective and indisputable ground truth. Thus, our paradigm invites and documents inter-annotator controversy based on a transparent set of analytical criteria, and aims at putting forth the consensual solutions emerging from such deliberations. The workflow that we suggest traces the entire genesis of annotation data, including the relevant discussions between annotators, reviewers, and curators. It adopts a well-proven pattern from collaborative software development, namely distributed version control, and allows for the automation of repetitive maintenance tasks, such as validity checks, message dispatch, or updates of meta- and paradata. To demonstrate the workflow's effectiveness, we introduce one possible implementation through GitHub Actions and showcase its success in creating cadence, phrase, and harmony annotations for a corpus of 36 trio sonatas by Arcangelo Corelli. Both code and annotated scores are freely available and the implementation can be readily used in and adapted for other MIR projects.

2021

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The Annotated Mozart Sonatas: Score, Harmony, and Cadence

Johannes Hentschel, Markus Franz Josef Neuwirth, Martin Alois Rohrmeier

This article describes a new expert-labelled dataset featuring harmonic, phrase, and cadence analyses of all piano sonatas by W.A. Mozart. The dataset draws on the DCML standard for harmonic annotation and is being published adopting the FAIR principles of Open Science. The annotations have been verified using a data triangulation procedure which is presented as an alternative approach to handling annotator subjectivity. This procedure is suited for ensuring consistency, within the dataset and beyond, despite the high level of analytical detail afforded by the employed harmonic annotation syntax. The harmony labels also encode contextual information and are therefore suited for investigating music theoretical questions related to tonal harmony and the harmonic makeup of cadences in the classical style. Apart from providing basic statistical analyses characterizing the dataset, its music theoretical potential is illustrated by two preliminary experiments, one on the terminal harmonies of cadences and the other on the relation between performance durations and harmonic density. Furthermore, particular features can be selected to produce more coarse-grained training data, for example for chord detection algorithms that require less analytical detail. Facilitating the dataset’s reusability, it comes with a Python script that allows researchers to easily access various representations of the data tailored to their particular needs.

2021

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