On 2020-03-19, Dario Sanfilippo wrote:
Thanks for your email, all good points. From the top of your head,
could you please point me to a reference for the measurement of
calculations needed in direct-form filters?
The best computational complexity in direct form convolution is
guaranteed to
Thanks for the clarification.
Sorry for the confusion re twiddle factors - I meant the per-bin complex
rotations, so I believe we're on the same page.
It's good to know that you found single precision floating pt to be
insufficient for long-term stability. The low-resolution fixed point
Hi, Ethan.
Thanks for your email, all good points. From the top of your head, could
you please point me to a reference for the measurement of calculations
needed in direct-form filters?
Best,
Dario
On Thu, 19 Mar 2020 at 17:28, Ethan Duni wrote:
>
>
> On Thu, Mar 19, 2020 at 8:11 AM Dario
(caveat - 13 years since I worked on this)
This is a real single-sample update Sliding DFT, not a block-based
method. The sample comes in, and used to perform a complex rotation to
each bin, followed by the frequency-domain convolution. There are no
twiddle factors as such. So the rectangular
> If simply comparing two algorithms by the number of operations needed to
> compute a sample, would you include delays in filters as an operation? I'm
> just wondering as some papers about FFT only include real multiplications
> and additions as operations.
>
It depends whether you are
Wow - interesting discussion.
I've implemented a real-time SDFT on an FPGA for use in carrier
acquisition of communications signals. It was surprisingly easy to do
and didn't require particularly massive resources, although FPGAs
naturally facilitate a degree of low-level parallelism that you
sorry for the repeats - don't know how that happened!
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In my original C programs it was all implemented in double precision
f/p, and the results were pretty clean (but we never assessed it
formally at the time), but as the computational burden was substantial
on a standard PC, there was no way to run them in real time to perform a
soak test.
Like many other things ….
Steffan
> On 19.03.2020|KW12, at 17:01, Ethan Fenn wrote:
>
> So interestingly those two #define's together would have no effect!
>
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On Thu, Mar 19, 2020 at 8:11 AM Dario Sanfilippo
wrote:
>
> I believe that the time complexity of FFT is O(nlog(n)); would you perhaps
> have a list or reference to a paper that shows the time complexity of
> common DSP systems such as a 1-pole filter?
>
The complexity depends on the topology.
On Tue, Mar 10, 2020 at 1:05 PM Richard Dobson wrote:
>
> Our ICMC paper can be found here, along with a few beguiling sound
> examples:
>
> http://dream.cs.bath.ac.uk/SDFT/
So this is pretty cool stuff. I can't say I've digested the whole idea yet,
but I had a couple of obvious questions.
In
As long as we're going off the rails...
This provoked me into learning something new:
https://stackoverflow.com/questions/24177503/how-does-the-c-preprocessor-handle-circular-dependencies
So interestingly those two #define's together would have no effect!
-Ethan
On Thu, Mar 19, 2020 at 7:34
Hello, list.
I would like to compare the efficiency of some feature extraction
algorithms based on FFT with some original time-domain algorithms used to
measure the same perceptual characteristics.
I believe that the time complexity of FFT is O(nlog(n)); would you perhaps
have a list or
#define analog digital
#define digital analog
and now read again ….
Best,
Steffan
> On 19.03.2020|KW12, at 12:31, Theo Verelst wrote:
>
> Maybe a side remark, interesting nevertheless: the filtering in digital
> domain, as
> compared with the analog good ol' electronics filters isn't the
Maybe a side remark, interesting nevertheless: the filtering in digital domain,
as
compared with the analog good ol' electronics filters isn't the same in any of
the
important interpretations of sampled signals being put on any regular digital to
analog converter, by and large regardless of the
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