The study of inter-annotator agreement in musical pattern annotations has gained increased attention over the past few years. While expert annotations are often taken as the reference for evaluating pattern discovery algorithms, relying on just one reference is not usually sufficient to capture the complex musical relations between patterns. In this paper, we address the potential of digital annotation tools to enable large-scale annotations of musical patterns, by comparing datasets gathered with two recently developed digital tools. We investigate the influence of the tools and different annotator backgrounds on the annotation process by performing inter-annotator agreement analysis and feature-based analysis on the annotated patterns. We discuss implications for further adaptation of annotation tools, and the potential for deriving reference data from such rich annotation datasets for the evaluation of automatic pattern discovery algorithms in the future.
Rock features extensive use of mediant mixture, that is, the use of both scale-degrees3̂and ♭3̂within a song; it also has been said to employ “blue notes” that fall between these two degrees. In this study we explore these issues, seeking to gain a better understanding of the use of mediant mixture and blue notes in rock. In addition to conventional aural analysis, we use an automatic pitch-tracking algorithm that identifies pitch contours with high accuracy. We focus on the Jackson 5’s “ABC”; several other songs and sections of songs are also considered briefly. Our tentative conclusions are that choices between3̂and ♭3̂in rock are complex but principled, guided by a small set of interacting preferences, and that blue notes are uncommon but do occasionally occur.
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