Machine Improvisation Through Generalized Transition Probability Graphs

Improvisation plays a cardinal role in the arts and is acknowledged to be a typical manifestation of creativity. In performing arts, an impromptu consists in playing extemporaneous sequences of actions, i.e.notes or movements, in accordance to some rules and constraints. Typically, a good improviser masters those constraints and can produce meaningful paths in the feasible space of allowed actions and can also explore some areas in the adjacencies of this space. From a computational perspective, one of the possible ways to capture this creative production is to make use of statistical learning mechanisms, which are also believed to be involved in human musical improvisation. At the basis of statistical learning are transitional probabilities between segments of a sequence and their following segments of symbols. In this paper we present preliminary results of a statistical learning model in which a transitional probability graph is computed from a set of sample pieces of music. This graph is subsequently generalized by applying a node similarity mechanism. This generalized graph is used for generating melodies that resemble improvisations in a given musical style.