Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations
This addresses the issue of annotator subjectivity in music analysis for researchers and practitioners, though it is incremental as it builds on existing chord estimation systems.
The paper tackles the problem of chord label personalization in automatic chord estimation by modeling subjectivity through deep learning of integrated harmonic interval-based representations, showing that using multiple reference annotations outperforms a single annotation.
The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.