The Annotated Mozart Sonatas: Score, Harmony, and Cadence

Abstract

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.

On the encoding of natural music in computational models and human brains

“…In computational musicology, music is often modeled as a sequence of symbols (e.g., a sequence of notes forms a melody of one part, a sequence of chords forms harmonic progressions and tonality). While this approach ignores multiple “unscored” variability in music signals including timbre, dynamics, and tempo, which are known to be very relevant to emotional responses and associated neural activity ( Chapin et al, 2010 ; Bresin and Friberg, 2011 ; Trochidis and Bigand, 2013 ), this approach enables scalable analyses on musical structures ( Rohrmeier and Cross, 2008 ; Moss et al, 2019 ; Rohrmeier, 2020 ; Hentschel et al, 2021 ). That is, once symbolic representations are collected, an analysis can be scaled up to a large volume of corpora using computers, a task that would take decades or more for human experts (musicologists) to complete.…”

Onset and Contiguity

“…Return to text 18. Marsden (2010), Katsiavalos, Collins, and Ba ey, (2019), Finkensiep et al (2020), andHentschel, Neuwirth, andRohrmeier (2021) also test their systems on Mozart piano sonatas. Note that the reading in Katsiavalos, Collins, and Ba ey (2019, 167) of the opening four mm.…”

A Clustering-Based Approach to Automatic Harmonic Analysis: An Exploratory Study of Harmony and Form in Mozart’s Piano Sonatas

“…This method has been used for Bach chorales (Jacoby et. al, 2015;Rohrmeier and Cross, 2008), textbook examples of common-practice tonal harmony (Temperley, 2001(Temperley, , 2009), Mozart's piano sonatas (Tymoczko, 2011;Henschel et. al., 2021), Mozart and Beethoven piano variations (Devaney et al, 2015), Beethoven's string quartets (Moss et al, 2019), and popular music (Harte et al, 2005;Burgoyne et al, 2013;DeClercq and Temperley, 2011;Temperley, 2018;Temperley and DeClercq, 2013), and comparative analysis of multiple such corpora (Sears and Forrest, 2021).…”