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Abstract This paper is a report of ongoing research on the computational modeling of beat induction which aims at achieving a better understanding of the perceptual processes involved by ordering and reformulating existing models. One family of rule-based beat induction models is described (Longuet-Higgins and Lee, 1982; Lee, 1985; Longuet-Higgins, 1994), along with the presentation of analysis methods that allow an evaluation of the models in terms of their in- and output spaces, ing from internal detail. It builds on work described in (Desain and Honing, 1994b). The present paper elaborates these methods and presents the results obtained. It will be shown that they can be used to characterize the differences between these models, a point that was difficult to assess previously. Furthermore, the first results of using the method to improve the existing rule-based models are presented, by describing the most effective version of a specific rule, and the most effective parameter settings.
Desain et al. (Fri,) studied this question.