Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.652623
Title: Nonlinear modelling of drum sounds
Author: Hovell, Simon A.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1994
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Abstract:
The aim of this work was to design a model of a simple drum that could reproduce all the nuances found in a real drum effectively and convincingly. In the past, this approach had often failed due to an inability to regenerate the very beginning of the sound - known as the percussive attack - successfully, possibly because of nonlinear information present in this part of the sound. One tool for detecting the presence of such nonlinear information is higher order spectral analysis. Detection of phase coupling between signals is one of the principal features of higher order signal analysis. It is shown that the presence of such phase coupling is a measure of dependence between different signal components. Furthermore, it is shown that examination of the power bispectrum can be used to detect the presence of nonlinear interactions between signals. Examination of the bispectra of a database of acoustic drum records gathered under strictly monitored conditions shows the presence of such interactions in the initial percussive attack. In order to exploit this information, it is necessary to use a nonlinear filter structure. Two such structures are examined, the Volterra filter, and the radial basis function network. It is found that the Volterra filter is capable of accurate reproduction of the percussive attack. Both filter structures suffer from a large degree of redundancy, and two techniques for reducing the size of the filters are successfully applied. It is seen that the simple least squares noise thresholding method performs better than the established orthogonal least squares algorithm, although at the cost of significant computational overhead.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.652623  DOI: Not available
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