[PD] efficient approximation of trig functions for hi pass formula (was: could vanilla borrow iemlib's hi pass filter recipe?)

[cyrille henry]

since you manually adjust the coefficient, i wanted to see the difference with coefficient adjusted/optimised by a computer.
I update the calc  using X from -0.5 to 0.5 with X = (x-0.5)*Pi
The coefficient i get are really different from yours (when considering the Pi factor between them).

I update you patch with this coefs.
I don't understand why, but your coefs works better for low frequency.
so, nothing better here, but I still think it's worth sharing, in case i'm not the only one wondering...


also, there was a typo in my original mail, it's not a linear regression, but a polynomial regression (since the result was obviously polynomial)

cheers
c

Le 19/10/2016 à 15:06, katja a écrit :
> Changing the thread title to reflect the new approach. Extract of the
> original thread;
>
> - I suggested using iemlib's hi pass filter recipe to improve
> frequency response of [hip~]
> - Christof Ressi pointed to formula in
> http://www.arpchord.com/pdf/coeffs_first_order_filters_0p1.pdf
> - this formula calculates feedback coefficient k = (1 - sin(a)) /
> cos(a) where a = 2 * pi * fc / SR
> - the filter implementation is y[n] = (x[n] - x[n-1]) * (1 + k) / 2
> +   k * y[n-1]
> - following convention in d_filter.c (and pd tilde classes in
> general), trig functions should best be approximated
> - Cyrille provided libre office linear regression result for (1-sin(x))/cos(x)
>
> Thanks for the useful infos and discussion. My 'math coach' suggested
> using odd powers of -(x-pi/2) in an approximation polynomial for
> (1-sin(x))/cos(x). The best accuracy/performance balance I could get
> is with this 5th degree polynomial:
>
> (-(x-pi/2))*0.4908 - (x-pi/2)^3*0.04575 - (x-pi/2)^5*0.00541
>
> Using this approximation in the filter formula, response at cutoff
> frequency is -3 dB with +/-0.06 dB accuracy in the required range 0 <
> x < pi. It can be efficiently implemented in C, analogous to an
> approximation Miller uses in [bp~]. So that is what I'll try next.
>
> Attached patch hip~-models.pd illustrates and compares filter recipes
> using vanilla objects:
>
> - current implementation, most efficient, accuracy +/- 3 dB
> - implementation with trig functions, least efficient, accuracy +/- 0.01 dB
> - implementation with approximation for trig functions, efficient,
> accuracy +/- 0.06 dB
>
> A note on efficiency: coefficients in [hip~] are only recalculated
> when cutoff frequency is changed. How important is performance for a
> function rarely called? I'm much aware of the motto 'never optimize
> early', yet I spent much time on finding a fast approximation, for
> several reasons: it's a nice math challenge, instructive for cases
> where performance matters more, and I want to respect Miller's code
> efficiency when proposing a change. Today pd is even deployed on
> embedded devices so the frugal coding approach is still relevant.
> After 20 years.
>
> Katja
>
>
> On Tue, Oct 18, 2016 at 10:28 AM, cyrille henry <ch at chnry.net> wrote:
>>
>>
>> Le 18/10/2016 à 00:47, katja a écrit :
>>>
>>> The filter recipe that Christof pointed to was easy to plug into the C
>>> code of [hip~] and works perfectly. But when looking further in
>>> d_filter.c I came across an approximation function 'sigbp_qcos()' used
>>> in the bandpass filter. It made me realize once more how passionate
>>> Miller is about efficiency. I'm not going to make a fool of myself by
>>> submitting a 'fix' using two trig functions to calculate a filter
>>> coefficient when a simple approximation could do the job. So that is
>>> what I'm now looking into, with help of a math friend: an efficient
>>> polynomial approximation for (1-sin(x))/cos(x).
>>
>> according to libre office linear regression, for x between 0 and Pi,
>> (1-sin(x))/cos(x) is about :
>> -0.057255x³ + 0.27018x² - 0.9157x + 0.99344
>>
>> the calc is in attachment, if you want to tune the input source or
>> precision.
>> cheers
>> c
>>
>>
>> _______________________________________________
>> Pd-list at lists.iem.at mailing list
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