Exploring annotations for musical pattern discovery gathered with digital annotation tools

The study of inter-annotator agreement in musical pattern annotations has gained increased attention over the past few years. While expert are often taken as reference for evaluating discovery algorithms, relying on just one is not usually sufficient to capture complex relations between patterns. In this paper, we address potential digital annotation tools enable large-scale patterns, by comparing datasets gathered with two recently developed tools. We investigate influence and different annotator backgrounds process performing analysis feature-based annotated discuss implications further adaptation tools, deriving data from such rich evaluation automatic algorithms future.