Difference between revisions of "Gesture Signal Processing"
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</center> | </center> | ||
+ | We can approximate integration and differentiation by using digital filters. Let '''q''' be the input signal and '''p''' be the output signal. | ||
− | + | Here is a simple approximation of an integrator. We see that with each time step, '''p<sub>intgr</sub>''' is updated to be nearly the same as the previous '''p<sub>intgr</sub>''', but it is affected by the input '''q'''. | |
<pre> | <pre> | ||
− | + | p<sub>intgr</sub> = 0.1*q + 0.9*p<sub>intgr</sub>; | |
</pre> | </pre> | ||
example of lp filter | example of lp filter | ||
− | + | ||
+ | Next we show how to approximate a differentiator. The extra variable '''r''' is introduced to represent the previous input value. | ||
<pre> | <pre> | ||
− | + | p<sub>diff</sub>[n] = q[n] - q[n-1] | |
</pre> | </pre> | ||
example of hp filter | example of hp filter | ||
[http://ccrma.stanford.edu/~jos/filters/ book on simple filter design] | [http://ccrma.stanford.edu/~jos/filters/ book on simple filter design] | ||
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== Thresholding == | == Thresholding == |
Revision as of 17:38, 10 October 2008
Interpolation
Filtering
While studying sensors, we discovered that often a particular sensor will measure the position x, velocity v, or acceleration a of an object. However, we might like to use a different variable to control the way we synthesize sound. Ideally, integration and differentiation can be applied to convert between variables.
We can approximate integration and differentiation by using digital filters. Let q be the input signal and p be the output signal.
Here is a simple approximation of an integrator. We see that with each time step, pintgr is updated to be nearly the same as the previous pintgr, but it is affected by the input q.
p<sub>intgr</sub> = 0.1*q + 0.9*p<sub>intgr</sub>;
example of lp filter
Next we show how to approximate a differentiator. The extra variable r is introduced to represent the previous input value.
p<sub>diff</sub>[n] = q[n] - q[n-1]
example of hp filter