A neuro-fuzzy system for sequence alignment on two levels

Abstract

The similarity judgement of two sequences is often decomposed in similarity judgements of the sequence events with an alignment process. However, in some domains like speech or music, sequences have an internal structure which is important for intelligent processing like similarity judgements. In an alignment task, this structure can be reflected more appropriately by using two levels instead of aligning event by event. This idea is related to the structural alignment framework by Markman and Gentner. Our aim is to align sequences by modelling the segmenting and matching of groups in an input sequence in relation to a target sequence, detecting variations or errors. This is realised as an integrated process, using a neuro-fuzzy system. The selection of segmentations and alignments is based on fuzzy rules which allow the integration of expert knowledge via feature definitions, rule structure, and rule weights. The rule weights can be optimised effectively with an algorithm adapted from neural networks. Thus, the results from the optimisation process are still interpretable. The system has been implemented and tested successfully in a sample application for the recognition of musical rhythm patterns.