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 annotations have been verified using a data triangulation procedure which is presented as an alternative approach to handling annotator subjectivity. The dataset draws on the DCML standard for harmonic annotation and is being published adopting the FAIR principles of Open Science. This article describes a new expert-labelled dataset featuring harmonic, phrase, and cadence analyses of all piano sonatas by W.A. The revealed inconsistencies in the results do not only indicate limitations of the employed chord labeling strategies but also deepen the understanding of the underlying music material. As a further contribution of this paper, we extend this principle by introducing a multi-perspective evaluation framework for simultaneously comparing chord recognition results over multiple performances of the same piece of music.
Using music synchronization techniques, the general idea is to locally warp the annotations of all given data streams onto a common time axis, which then allows for a cross-domain evaluation of the various types of chord labels. In this paper, we bridge this gap by describing a procedure that allows for transferring annotations and chord labels from the score domain to the audio domain and vice versa. On the other hand, trained musicians can easily derive chord labels from symbolic score data.
Lilypond chords seventh manual#
However, the manual generation of such annotations on the basis of audio recordings is tedious and time-consuming. To evaluate computer-based chord labeling procedures, one requires ground truth annotations for the underlying audio material. The automated extraction of chord labels from audio recordings constitutes a major task in music information retrieval.
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