A Musical Pattern Discovery System Founded on a Modeling of Listening Strategies

Music is a domain of expression that conveys a paramount degree of complexity. The musical surface, composed of a multitude of notes, results from the elaboration of numerous structures of different types and sizes. The composer constructs this structural complexity in a more or less explicit way. The listener, faced by such a complex phenomenon, is able to reconstruct only a limited part of it, mostly in a non-explicit way. One particular aim of music analysis is to objectify such complexity, thus offering to the listener a tool for enriching the appreciation of music (Lartillot and SaintJames, 2004). The trouble is, traditional musical analysis, although offering a valuable understanding of musical style, does not go into the deepest details of this complexity. Some approaches of 20th-century musicology, such as the thematic analysis by Rudolph Reti (1951), were aimed at a better awareness of complexity. However, their scope was still restricted to a particular aspect of musical structure. For instance, Reti’s approach was founded on the hypothesis that a musical work is built on a single motive. And even within such limited scope, the search cannot be undertaken exhaustively, owing to the unreachable combinatory structure of musical works. Even worse, the results of such analyses do not meet a consensus agreement (Cook 1987), which questions the relevance of the underlying methods. Nicolas Ruwet formalized motivic analysis as a set of partially detailed operations that carry out a top–down hierarchical segmentation of the musical work (Ruwet 1987). However, he never actually followed his model when applying it to concrete examples, but rather he relied implicitly on his own intuitions. In fact, a careful application of this method easily leads to absurd results that invalidate the model (Lartillot 2004). All this points to the necessity of a computational modeling of the discovery processes themselves. Indeed, a computer implementation of the modeling can explicitly show its potential capacities and pertinence and can be validated—or invalidated—according to its operational efficiency. Moreover, non-computational modeling implicitly tends to limit the complexity of algorithms and data structures for practical reasons. For instance, the grouping structure proposed by Lerdahl and Jackendoff (1983), which can successfully be implemented on a computer (Temperley 2001), relies however on the foundations of simple preference rules and the idea of a unique hierarchical system, which, as will be explained below, limits a detailed understanding of musical structure. The necessary simplifications that are required by the non-computer modeling question the use of modeling itself, according to Reti: