Abstract:

This paper presents a new model for measuring similarity in a general Rhythm Space. Similarity is measured by establishing a comparison between subsequences of a given rhythm. We introduce the hierarchical subdivision of rhythm sequences in several levels, and compute a Distance Matrixfor each level using the "block distance". The information about the similarity of the rhythmic substructures is retrieved from the matrices and coded into a Similarity Coefficient Vector (SCV). Wealso present possibilities for the reduction to single values of similarity derived from the SCV. In addition, two applications of the formal model are presented, showing the potential for development using this approach.