Free Books

Digital Waveguide Models

The digital waveguide modeling paradigm was developed in 1985-86 as a guaranteed-stable ``construction kit'' for lossless reverberator prototypes supporting general feedback topologies [430]. The following year, digital waveguide building blocks were extended to vibrating strings and single-cylinder acoustic tubes (principally for the clarinet family) [431]. A modular synthesis architecture was developed in which various ``nonlinear junctions'' could be used to excite digital waveguide networks of general design. The iterative Friedlander-Keller solver of [308] was replaced by a look-up table plus a couple of additions and a multiply, facilitating sound synthesis in real time. This computational model was presented, with sound examples, at the 1986 ICMC, and it so happened that Yamaha's chief engineer was in the audience. Perhaps significantly, the FM synthesis patent was nearing the end of its life. Yamaha soon hired some consultants to evaluate waveguide synthesis, and in 1989 they began a strenuous development effort culminating in the VL1 (``Virtual Lead'') synthesizer family, introduced in the U.S. at the 1994 NAMM show in LA. The jazz trio demonstrating the VL1 at NAMM created quite a ``buzz'', and the cover of Keyboard Magazine soon proclaimed it as ``The Next Big Thing'' in synthesis technology [375]. As it happened, physical modeling synthesis did not immediately meet with large commercial success, perhaps because wavetable synthesis required less computation and was so much easier to ``voice'', and because memory was fairly inexpensive. The market at large simply has not yet demanded more expressive sound synthesis algorithms than what wavetable synthesis can provide. However, the quality of the VL1 in the hands of skilled players constituted proof-of-concept for many of us, and helped stimulate further academic interest in the approach. Trained performing musicians generally prefer the expressive richness of a computational physical model, and find wavetable synthesis to be comparatively limiting, as typically implemented.

Next Section:
Previous Section:
Karplus-Strong Algorithms