AI & ML
A General Theoretical Framework for Transformed Geometric Pattern Matching in Music
arXiv cs.SD0 views
arXiv:2610.42095v1 Announce Type: new Abstract: In this paper, we present a framework of concepts, terminology and notation for geometric pattern matching, centred around the idea of a {\em transformation class}. After formally defining the concept of a transformation class, we provide detailed definitions of a number of specific transformation classes that are particularly relevant to pattern matching and pattern discovery in music, which is the primary application domain within which the framework has been developed. We then provide formal definitions of the notions of {\em match}, {\em occurrence}, {\em maximal match\/} and {\em maximal occurrence\/} and prove some results relating to these concepts. We then define and explore in detail the idea of an {\em occurrence set\/} and the more general notion of a {\em heterogeneous occurrence set}. Finally, we explore in detail the concept of {\em transformational equivalence}, including a consideration of {\em transformation groups}, along with the group-theoretic and transformational equivalence properties of selected transformation classes.
Source：https://arxiv.org/abs/2610.42095PreviousSelf-Supervised Object-Centric Representation for Multi-Task Audio AnalysisNextIncongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models
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