Automated Motivic Analysis: An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces

Abstract

Motivic analysis provides very detailed understanding of musical compositions, but is also particularly difficult to formalize and systematize. A computational automation of the discovery of motivic patterns cannot be reduced to a mere extraction of all possible sequences of descriptions. The systematic approach inexorably leads to a proliferation of redundant structures that needs to be addressed properly. Global filtering techniques cause a drastic elimination of interesting structures that damages the quality of the analysis. On the other hand, a selection of closed patterns allows for lossless compression. The structural complexity resulting from successive repetitions of patterns can be controlled through a simple modelling of cycles. Generally, motivic patterns cannot always be defined solely as sequences of descriptions in a fixed set of dimensions: throughout the descriptions of the successive notes and intervals, various sets of musical parameters may be invoked. In this chapter, a method is presented that allows for these heterogeneous patterns to be discovered. Motivic repetition with local ornamentation is detected by reconstructing, on top of “surface-level” monodic voices, longer-term relations between non-adjacent notes related to deeper structures, and by tracking motives on the resulting syntagmatic network. These principles are integrated into a computational framework, the MiningSuite, developed in Matlab.