Melodic Contour and Mid-Level Global Features Applied to the Analysis of Flamenco Cantes
This work addresses the problem of analyzing traditional music collections for researchers and musicians, but it is incremental as it applies existing methods to a specific domain.
The paper tackled melodic characterization and similarity in a cappella flamenco singing by combining automatic transcription with expert-labeled mid-level features to create a global similarity measure, which was evaluated through clustering and style categorization.
This work focuses on the topic of melodic characterization and similarity in a specific musical repertoire: a cappella flamenco singing, more specifically in debla and martinete styles. We propose the combination of manual and automatic description. First, we use a state-of-the-art automatic transcription method to account for general melodic similarity from music recordings. Second, we define a specific set of representative mid-level melodic features, which are manually labeled by flamenco experts. Both approaches are then contrasted and combined into a global similarity measure. This similarity measure is assessed by inspecting the clusters obtained through phylogenetic algorithms algorithms and by relating similarity to categorization in terms of style. Finally, we discuss the advantage of combining automatic and expert annotations as well as the need to include repertoire-specific descriptions for meaningful melodic characterization in traditional music collections.