Subtractive Synthesis

If one is interested in producing sounds with rich spectra, additive synthesis, requiring a separate oscillator for each desired frequency component, can obviously become quite a costly undertaking. Instead of building up a complex sound, one partial at a time, another way of proceeding is to begin with a very rich sound, typically simple to produce and lacking in character such as white noise or an impulse train, and then shape the spectrum using digital filtering methods. This technique is often referred to as subtractive synthesis--see Figure 1.3. It is especially powerful when the filtering applied is time-varying, allowing for a good first approximation to the timbre of many musical tones of unsteady timbre (this is generally the norm).

Subtractive synthesis is often associated with physical models [146], but this association is a very loose one at best. What is meant is that many linear models of sound production may be broken down into source and filtering components. This is particularly true of models of human speech, in which case the glottis is assumed to produce a wide band signal (i.e., a signal somewhat like an impulse train under voiced conditions, and white noise under unvoiced conditions) which is filtered by the vocal tract, yielding a spectrum with pronounced peaks (formants) which indicate a particular vocal timbre. In this book, however, because of the emphasis on time domain methods, the source/filter methodology will not be explicitly employed. Indeed, for distributed nonlinear problems, which do not allow for meaningful frequency domain analysis, it is of little use. Still, the breakdown of a system into an lumped/distributed pair representing an excitation mechanism and the instrument body is a very powerful one, even if, in some cases, the behaviour of the body cannot be explained in terms of filtering concepts.

Figure 1.3: Subtractive synthesis.