Revistes Catalanes amb Accés Obert (RACO)

A neuro-fuzzy system for sequence alignment on two levels

Tilman Weyde, Klaus Dalinghaus

Resum


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.

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