Casey, Michael A.. 1994. Understanding Musical Sound with Forward Models and Physical Models. Connection Science, 6(2-3), pp. 355-371. ISSN 0954-0091 [Article]

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Abstract or Description

This research report describes an approach to parameter estimation for physical models of sound-generating systems using distal teachers and forward models (Jordan & Rumelhart, 1992; Jordan, 1990). The general problem is to find an inverse model of a sound-generating system that transforms sounds to action parameters; these parameters constitute a model-based description of the sound. We first show that a two-layer feedforward model is capable of performing inverse mappings for a simple physical model of a violin string. We refer to this learning strategy as direct inverse modeling; it requires an explicit teacher and it is only suitable for convex regions of the parameter space. A model of two strings was implemented that had non-convex regions in its parameter space. We show how the direct modeling strategy failed at the task of learning the inverse model in this case and that forward models can be used, in conjunction with distal teachers, to bias the learning of an inverse model so that non-convex regions are mapped to single-point solutions in the parameter space. Our results show that forward models are appropriate for learning to map sounds to parametric representations.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1080/09540099408915730
Departments, Centres and Research Units:	Computing
Dates:	Date Event 1994 Published
Item ID:	15154
Date Deposited:	01 Dec 2015 14:31
Last Modified:	20 Jun 2017 09:43
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/15154

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