A Multi-Perspective Evaluation Framework for Chord Recognition

The automated extraction of chord labels from audio recordings constitutes a major task in music information retrieval. To evaluate computer-based chord labeling procedures, one requires ground truth annotations for the underlying audio material. However, the manual generation of such annotations on the basis of audio recordings is tedious and time-consuming. On the other hand, trained musicians can easily derive chord labels from symbolic score data. 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. 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. 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. 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.