CCRMA OVERVIEW - CCRMA - Stanford University
CCRMA OVERVIEW - CCRMA - Stanford University
CCRMA OVERVIEW - CCRMA - Stanford University
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A chaotic signal is generated by a mathematical expression that contains some sort of randomness.<br />
The periodicity of the values given by the expression depends upon probability distributions and rules<br />
which also are dependent on past or present values. The behavior of these functions show symmetry or<br />
tendency causing attraction to the value with the highest periodicity. A chaotic behavior is considered<br />
as a quantified level of unpredictability [Schroeder, 1991] which needs to be normalized and mapped to<br />
values meaningful and within the bounds for synthesis of Physical Models.<br />
Examples<br />
Musical gestures can be produced on the beat or event scope but they can also be part of melodic<br />
phrases, as articulations of combinations of musical notes. Consequently a parameter for expression<br />
must be chosen along, keeping in mind that changes developed, are either perceived as mutations in<br />
duration or spectra. A mathematical expression or a set of rules or probabilities give values that change<br />
always as function of time. Examples of functions which provide a variety of numbers for musical<br />
parameter manipulation in Physical Models are:<br />
The random function R[n] which returns an integer greater than zero and less than or equal to the given<br />
value n. This function is the basis for creating Random Walks or different ways ordering and sorting of<br />
a musical event.<br />
The Henon Map: [Weeks, 1999]<br />
X[n+1]= (AX[n])squared + BY[n] y[n+l]= X[n]<br />
is an example of a set of rules for generating periodic, quasi periodic and chaotic signal which can be<br />
mapped to a set of N pitches in a sequence of notes. Careful choice of values A and B manages the<br />
behavior of the signal<br />
The Restrepo Map: [Restrepo, 1997]<br />
X[n+1]= X[n]*(X[n]-l)/X[n-l])<br />
is a result of Teager's filter and gives a periodic predictable region plus a chaotic region. Values different<br />
to 0 and 1 and on the intervals X[0]=0.01, x[l] = 1.99 ; x[0] = 1.99, x[l]=1.99 can be mapped to rhythmic<br />
and drumming patterns in addition to a sequence of pitches.<br />
The Henon Map and the Restrepo map are two eamples of recursive functions that can be mapped to<br />
midinote values using the following normalizing function suggested by Craig Sapp [Sapp, 2000]:<br />
INT[(x+l)/2 * 127+1/2]<br />
Results<br />
Phrasing and melodic transformations are another class of music gesture which permit use of chaotic<br />
expressions. In this, the relationship among a sequence of musical events, past, present and future also<br />
determine expressiveness. A group or combination of notes describes not only a melody but how these<br />
notes are tied together[1995, Jaffe, Smith]. There can be a function or rule which conducts how these<br />
notes are glued together. Thus it can specify durations of musical events as well as space (silence)<br />
between each event.<br />
When a chaotic or Random function is applied to the frequency (pitch) of a sound, different shades of<br />
noise are obtained. Nevertheless these values can also be applied to filter coefficients providing musical<br />
effects. For example, In the case of the Physical Model of the Piano, they can handle detunning factor,<br />
stiffness, hammer strike angle, etc. The advantage of using chaos is that the effect seems more natural<br />
and reflects the state (energy or entropy) of a real musical instrument.<br />
Conclusions<br />
It is important to understand that a musical instrument is not a stable or balanced system in reality.<br />
Its behavior and responsiveness depends on various factors and consequently a Physical Model should<br />
not be framed as a squared box or a fixed parametric system. The degree of musical expression is<br />
proportional to the number of degrees of freedom provided by the Physical Model. Nevertheless the<br />
complexity of the model is inverse to its degrees of freedom. Therefore, the more flexible the model<br />
the harder to understand and handle. More information and parameter descriptions and documentation<br />
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