filters 2.0
I've gone on quite long enough about filters. But I think I'm finally getting it right. So it's my blog, after all, and the whole point is I can write about what I want. So I'm going to stick with filters a bit longer... I'm trying to figure this stuff out for myself.
As I noted last time, after thinking about it, I realized I could do all the filtering I needed with strictly trigonometric functions, and trigonometric functions have attractive mathematical features (particularly, their exceptional smoothness, and their tendency to average to zero). It's just a matter of selecting the function. The goal is to smooth the data (reduce high frequency components) while optionally also reducing low frequency components.
The criteria are:
Let me clarify the last point: the process is to define a function which is zero for some t > t0. Then to shift it by ‒t0 such that for all positive (future) times the function is zero. This allows for a causal filter. We want the represent "recent" values of the data so that it responds to changes in the data in "real time" - this is why we want a causal filter. So we want the mean time of the contribution to the result to be τ. The exponential function which fulfills this requirement is exp(-Δt/τ).
So here's the functions I'll pick. In each case the function is non-zero from -τ to τ. Then it can be shifted by -τ to make it causal. This constraint is a bit different than what I've used to this point, the difference by scaling the Δt axis and then scaling the f-axis to keep the area the same.
The first one is one I considered before, with different scaled by π/2, except here I won't do any goofy differentiation. The second is very similar to the "Δt/τ cos² Δt/τ" filter I used before, but this time using only trigonometric functions, no multiplying by Δt/τ. The third is new. It's designed to be particularly good at filtering low frequencies, since it will convert not just a constant value but also a straight line to zero after convolution due to its symmetry.
I'll look at the frequency responses next time.
As I noted last time, after thinking about it, I realized I could do all the filtering I needed with strictly trigonometric functions, and trigonometric functions have attractive mathematical features (particularly, their exceptional smoothness, and their tendency to average to zero). It's just a matter of selecting the function. The goal is to smooth the data (reduce high frequency components) while optionally also reducing low frequency components.
The criteria are:
- The area of the absolute value of the function should be one.
- The function needs to transition to zero smoothly, which is to say with a continuous slope, at the edges. This helps to reduce high frequency components.
- If the function is to attenuate low frequency components, its average value should be zero, else the function should be strictly positive.
- The function, after being made causal, should have a centroid at time -τ. This is to keep a constraint of constant delay compared with exponential smoothing.
- If the function is antisymmetric, it should have a positive slope at its center with respect to real time, negative with respect to a Δt which goes into the past. This is a bit arbitrary, but tends to make the function respond positively to positive transitions in the data.
Let me clarify the last point: the process is to define a function which is zero for some t > t0. Then to shift it by ‒t0 such that for all positive (future) times the function is zero. This allows for a causal filter. We want the represent "recent" values of the data so that it responds to changes in the data in "real time" - this is why we want a causal filter. So we want the mean time of the contribution to the result to be τ. The exponential function which fulfills this requirement is exp(-Δt/τ).
So here's the functions I'll pick. In each case the function is non-zero from -τ to τ. Then it can be shifted by -τ to make it causal. This constraint is a bit different than what I've used to this point, the difference by scaling the Δt axis and then scaling the f-axis to keep the area the same.
The first one is one I considered before, with different scaled by π/2, except here I won't do any goofy differentiation. The second is very similar to the "Δt/τ cos² Δt/τ" filter I used before, but this time using only trigonometric functions, no multiplying by Δt/τ. The third is new. It's designed to be particularly good at filtering low frequencies, since it will convert not just a constant value but also a straight line to zero after convolution due to its symmetry.
I'll look at the frequency responses next time.
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