Abstract
A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of classes in top k pattern mining and in individual top ranked patterns. Characteristic pattern mining is applied to the collection of Native American music by Frances Densmore, and the discovered patterns are shown to be supported by Densmore’s own analyses.
Highlights
Advances in music data mining and the creation of annotated music corpora [1,2,3] have supported a renewed interest in comparative music analysis [4,5]
This paper explores characteristic pattern mining for music corpus analysis and investigates the trade-off between completeness and discriminant power of descriptive patterns
The following two sections present example patterns discovered in the two case studies on Densmore’s collection of Native American songs. They serve to illustrate the findings of the previous section at the level of individual patterns, to assess the pattern discovery against related observations by Densmore, and to demonstrate characteristic pattern mining for music corpus analysis
Summary
Advances in music data mining and the creation of annotated music corpora [1,2,3] have supported a renewed interest in comparative music analysis [4,5]. Analyses of class-labeled music datasets have explored a range of data mining paradigms, including descriptive methods such as subgroup discovery and emerging pattern mining [6,7,8,9,10,11,12] and predictive methods such as decision tree and classification rule induction [13,14,15,16,17,18]. These studies generally focus on identifying discriminant properties, which distinguish different classes.
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