Warped Linear Prediction of Physical Model Excitations with Applications in Audio Compression and Instrument Synthesis
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Warped Linear Prediction of Physical Model Excitations with Applications in Audio Compression and Instrument Synthesis Alexis Glass Department of Acoustic Design, Graduate School of Design, Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka 815-8540, Japan Email: [email protected]
Kimitoshi Fukudome Department of Acoustic Design, Faculty of Design, Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka 815-8540, Japan Email: [email protected] Received 8 July 2003; Revised 13 December 2003 A sound recording of a plucked string instrument is encoded and resynthesized using two stages of prediction. In the first stage of prediction, a simple physical model of a plucked string is estimated and the instrument excitation is obtained. The second stage of prediction compensates for the simplicity of the model in the first stage by encoding either the instrument excitation or the model error using warped linear prediction. These two methods of compensation are compared with each other, and to the case of single-stage warped linear prediction, adjustments are introduced, and their applications to instrument synthesis and MPEG4’s audio compression within the structured audio format are discussed. Keywords and phrases: warped linear prediction, audio compression, structured audio, physical modelling, sound synthesis.
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INTRODUCTION
Since the discovery of the Karplus-Strong algorithm [1] and its subsequent reformulation as a physical model of a string, a subset of the digital waveguide [2], physical modelling has seen the rapid development of increasingly accurate and disparate instrument models. Not limited to string model implementations of the digital waveguide, such as the kantele [3] and the clavichord [4], models for brass, woodwind, and percussive instruments have made physical modelling ubiquitous. With the increasingly complex models, however, the task of parameter selection has become correspondingly difficult. Techniques for calculating the loop filter coefficients and excitation for basic plucked string models have been refined [5, 6] and can be quickly calculated. However, as the one-dimensional model gave way to models with weakly interacting transverse and vertical polarizations, research has looked to new ways of optimizing parameter selection. These new methods of optimizing parameter selection use neural networks or genetic algorithms [7, 8] to automate tasks which would otherwise take human operators an inordinate amount of time to adjust. This research has yielded more accurate instrument models, but
for some applications it also leaves a few problems unaddressed. The MPEG-4 structured audio codec allows for the implementation of any coding algorithm, from linear predictive coding to adaptive transform coding to, at its most efficient, the transmission of instrument models and performance data [9]. This coding flexibility means that MPEG4 has the potential to implement any coding algorithm and to be within an order of magnitude of the most efficient codec for any given input
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