Reduce Ringing in FIRs

On Wednesday, December 16, 2015 at 1:26:58 PM UTC-6, Steve Pope wrote:

> One thing I have done in this type situation is, assuming I
> have a reaosnably capable filter-design program, run the
> program over a large range of slightly different parameters
> (number of taps, passband ripple, start/stop frequencies, etc.)
> and evaluate each for the issue in question (in your case,
> ringing).

The classical approach is to design the frequency response and then evaluate the resulting time domain response. Don't forget duality -- design the impulse response and then evaluate the resulting frequency response. It might be a faster path to an acceptable solution. 

Harris' "windows" paper might be a good starting point: 
https://www.utdallas.edu/~cpb021000/EE%204361/Great%20DSP%20Papers/Harris%20on%20Windows.pdf

Greg

On Tue, 15 Dec 2015 18:05:58 -0500, Randy Yates
<yates@digitalsignallabs.com> wrote:

>Gentle comp.dsp readers,
>
>A requirement just popped up in one of my projects to redesign an FIR
>lowpass filter so that it doesn't create so much time-domain ringing on
>spikes. I had originally used octave's firls to design a linear-phase
>lowpass filter (Fs = 625 Hz, Fp = 100 Hz, Fs = 150 Hz), but its response
>has got a lot of wigglies.
>
>Any thoughts/papers/google searches/bickering/smart remarks/dumb
>ideas/etc. appreciated.

Randy, Just a couple tangential thoughts:

The high end of the audio industry has been pursuing solutions to
ringing, including use of minimal phase filters to push the ringing to
an area that will be masked, or at least perceived as less intrusive.
There has been mention of 'apodizing filtters' for this purpose. I'm
not sure why that would need to be a separate filter, and it's
certainly no free lunch.  But companies like Meridian have been
pursuing it, and they're not typical purveyers of mystical audio
voodoo.

Or perhaps some type of adaptive filter?  You already know that you're
working with a specific response (QRS), so there may be nonlinear
transforms that can be adapted.  I imagine that most signals fit a
finite set of profiles.

Or how about neural networks? They're being used more often for signal
processing apps now.  A shot in the dark: There's a book by Timothy
Masters called "Signal and Image Processing with Neural Networks"
(ISBN 0471049638) that may have something.  I know he deals with
statistical filtering methods.  That book is rather old (comes with a
diskette!) but it's inexpensive and the data is relevant.  Another of
his books: "Neural, Novel & Hybrid Algorithms for Time Series
Prediction" may also be useful.  Amazon has a preview of the latter.
Source code for both on his site: http://timothymasters.info/

The thing that occurred to me is the possibility of doing analysis of
the waveform using a neural net.  They're incredibly good at pattern
matching, so you may be able to train a simple network with healthy
profiles and known aberrations, and get some idea of a match with new
input.  It is being done with other medical signals, and even
radiographs.

BTW, I have both of the Timothy Masters books somewhere.  If you have
specific questions, I'll dig 'em up and take a look.  You might also
sound Timothy out about this,as he knows both sides of the tracks.