AN ADAPTIVE MULTI-PARAMETRIC AND REDUNDANCY-FILTERING APPROACH FOR MOTIVIC PATTERN DISCOVERY

Publication Type:

Conference Paper

Source:

SMC Conference 2004 (2004)

URL:

files/proceedings/2004/P20.pdf

Abstract:

This paper presents the principles of a new approach aimed at automatically discovering motivic patterns in monodies. It is shown that, for the results to agree with the listener’s understanding, computer modelling needs to follow as closely as possible the strategies undertaken during the listening process. Motivic patterns, which may progressively follow different musical dimensions, are discovered through an adaptive incremental identification in a multi-dimensional parametric space. The combinatorial redundancy that would logically result from the model is carefully limited with the help of particular heuristics. In particular, a notion of specificity relation between pattern descriptions is defined, unifying suffix relation – between patterns – and inclusion relation – between the multi-parametric descriptions of patterns. This enables to discard redundant patterns, whose descriptions are less specific than other patterns and whose occurrences are included in the occurrences of the more specific patterns. Periodic repetitions of patterns also induce combinatory proliferations of redundant patterns, which are avoided by modelling patterns as cyclic chains of states. Resulting analyzes come close to the structures actually perceived by the listener.