of Microprocessors

548 MUSICAL ApPLICATIONS OF MICROPROCESSORS
(G)
Fig. 16-1. Methods of representing three-dimensional spectral data in two
dimensions (cont.). (G) Contour map (voiceprint).
the figure is drawn accurately, this is one of the better quantitative methods
of representing spectral data, since all three variables can be very easily
measured on the plot with a compass and ruler.
One can also fill the rectangle with a patch of gray (or light if on a
CRT). The density of gray or brightness of light represents amplitude as in
Fig. 16---1D. This is commonly called a sound spectrogram, a name coined by a
company that makes analog equipment for drawing such plots. If one has a
color display, then a different color can be assigned to each amplitude quantization
level. Computer line printers can also be pressed into service as
spectrum plotters by defining time and frequency increments in terms ofline
and character positions. The rectangles are then fil1ed in with characters or
overprinted character combinations chosen for their apparent darkness.
In any case, this gives a very-easy-to-interpret visual representation of
the data, but measurement of amplitude from the graph is no longer possible.
A very clever compromise is illustrated in Fig. 16-1F in which 16
different "characters" based on the hexadecimal digits are defined. The idea
is to have the area covered by black in the character shape to correspond to its
numerical value. If the amplitude values are quantized to 16 levels, one can
merely read off the values while simultaneously having a "gray-scale" plot to
scan visually.
The final method is the familiar contour map approach to threedimensional
plotting as in Fig. 16---1G. When such plots are made of speech
spectra, they are often called "voiceprints" because of the resemblance in
form to fingerprints (there is some controversy over whether they are nearly
as distinctive as fingerprints). This is perhaps the most accurate method of
representation, particularly if changes in the spectrum are slow. U nfortunately,
visual interpretation in terms of features valuable for synthesis is
difficult.
Time-Frequency Resolution
In a spectral plot, good frequency resolution is desirable so that individual
har~onics of the sound are clearly distinguishable. Good time resolution
is also desirable so that the exact time of significant spectral events can