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References
• Levine. S.. "Audio Representations for Data Compression and Compressed Domain Processing".
Ph.D. thesis. Electrical Engineering Department. Stanford University fCCRMA). December 1996.
Available online at http://www-ccnna.stajiford.edu/~scottl/thesis.html.
• Sena. X.. and J. O. Smith. "Spectral Modeling Synthesis: A Sound Analysis/Synthesis System
Based on a Deterministic plus Stochastic Decomposition." Computer Music J . vol. 14. no. 4. pp.
12-24. Win.. 1990. The latest free Spectral Modeling Synthesis (SMS) software can be obtained
from the SMS home page at http://www.iua.upf.es/~sms.
• Smith. .1. O.. Music 420 (EE 367A) Course Description 4 . Stanford University.
6.2.13 Digital Waveguide Modeling of Acoustic Systems
Julius Smith
Digital Waveguide Filters (DWF) have proven useful for building computational models of acousticsystems
which are both physically meaningful and efficient for applications such as digital synthesis.
The physical interpretation opens the way to capturing valued aspects of real instruments which have
been difficult to obtain by more abstract synthesis techniques. Waveguide filters were initially derived to
construct digital reverberators out of energy-preserving building blocks, but any linear acoustic system
can be approximated using waveguide networks. For example, the bore of a wind instrument can be
modeled very inexpensively as a digital waveguide. Similarly, a violin string can be modeled as a digital
waveguide with a nonlinear coupling to the bow. When the computational form is physically meaningful.
it is often obvious how to introduce nonlinearities correctly, thus leading to realistic behaviors far beyond
the reach of purely analytical methods.
In this context, a waveguide can be defined as any medium in which wave motion can be characterized by
the one-dimensional wave equation. In the lossless case, all solutions can be expressed in terms of leftgoing
and right-going traveling waves in the medium. The traveling waves propagate unchanged as long
as the wave impedance of the medium is constant. At changes in the wave impedance, a traveling wave
partial transmits and partially reflects in an energy conserving manner, a process known as "scattering."
The wave impedance is the square root of the "massiness" times the "stiffness" of the medium; that is.
it is the geometric mean of the two sources of resistance to motion: the inertial resistance of the medium
due to its mass, and the spring-force on the displaced medium due to its elasticity.
Digital waveguide filters are obtained (conceptually) by sampling the unidirectional traveling waves which
occur in a system of ideal, lossless waveguides. Sampling is across time and space. Thus, variables in a
DWF structure are equal exactly (at the sampling times and positions, to within numerical precision)
to variables propagating in the corresponding physical system. Signal power is defined instantaneously
with respect to time and space (just square and sum the wave variables.) This instantaneous handle
on signal power yields a simple picture of the effects of round-off error on the growth or decay of the
signal energy within the DWF system. Because waveguide filters can be specialized to well studied
lattice/ladder digital filters, it is straightforward to realize any digital filter transfer function as a DWF.
Waveguide filters are also related to "wave digital filters'" (WDF) which have been developed primarily by
Fettweis. Using a "mesh" of one-dimensional waveguides, modeling can be carried out in two and higher
dimensions. In other applications, the propagation in the waveguide is extended to include frequency
dependent losses and dispersion. In still more advanced applications, nonlinear effects are introduced as
a function of instantaneous signal level.
Digital waveguide filters can be viewed as an efficient discrete-time "building material" for acoustic
models incorporating aspects of one-dimensional waveguide acoustics, lattice and ladder digital filters.
wave digital filters, and classical network theory.
Iittp ' / www-ccrtna.stanford.edu/CCRMA/Courses/420/
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