Phil Martel wrote:> On 1/18/2017 12:36, Tim Wescott wrote: >> On Wed, 18 Jan 2017 03:19:27 -0800, pedro1492 wrote: >> >>> On Wednesday, January 18, 2017 at 6:35:49 AM UTC+8, eric.j...@ieee.org >>> wrote: >>>> >>>> A frequency-selective filter removes information. Generally once that >>>> information is discarded (by the filter), it is no longer in the signal >>>> and can't be restored from the information left in the signal after the >>>> filter. >>>> >>>> >>> It depends: in my field, data is recorded at 24 bits, and inverse >>> filters are used. If it was Butterworth filter, the information is >>> squashed but not eliminated. But if an Ormsby filter were used, then >>> you are up shit creek. >> >> The issue can get cloudy if the noise is attenuated along with the >> desired signal. I'm working on what appears to be just such a system >> myself -- I'll find out in a few months to what extent I'm right. >> > Well, the problem is that it is a digital signal and is therefore > quantized. Reducing the signal level at a given frequency must make the > S/N ratio worse. >The same generally holds for analog :) It's just easier to model in sampled systems. -- Les Cargill
I have a sampled signal that was HP-filtered before sampling. Is there a way to revert the HP-filtering?
Started by ●January 17, 2017
Reply by ●January 19, 20172017-01-19
Reply by ●January 20, 20172017-01-20
On Thu, 19 Jan 2017 17:42:15 -0600, Les Cargill <lcargill99@comcast.com> wrote:>Phil Martel wrote: >> On 1/18/2017 12:36, Tim Wescott wrote: >>> On Wed, 18 Jan 2017 03:19:27 -0800, pedro1492 wrote: >>> >>>> On Wednesday, January 18, 2017 at 6:35:49 AM UTC+8, eric.j...@ieee.org >>>> wrote: >>>>> >>>>> A frequency-selective filter removes information. Generally once that >>>>> information is discarded (by the filter), it is no longer in the signal >>>>> and can't be restored from the information left in the signal after the >>>>> filter. >>>>> >>>>> >>>> It depends: in my field, data is recorded at 24 bits, and inverse >>>> filters are used. If it was Butterworth filter, the information is >>>> squashed but not eliminated. But if an Ormsby filter were used, then >>>> you are up shit creek. >>> >>> The issue can get cloudy if the noise is attenuated along with the >>> desired signal. I'm working on what appears to be just such a system >>> myself -- I'll find out in a few months to what extent I'm right. >>> >> Well, the problem is that it is a digital signal and is therefore >> quantized. Reducing the signal level at a given frequency must make the >> S/N ratio worse. >> > > >The same generally holds for analog :) It's just easier to model in >sampled systems.Good thing it was high pass filtered and wants to get some of the low end back rather than the other way around. The perceived noise would seem even worse I would imagine. boB
Reply by ●January 20, 20172017-01-20
Do you know anything about your signal in another domain (eg time limits, amplitude limits, non-negativity etc)? If so, you could try Alternating Projections, a simplified Projections Onto Convex Sets, (POCS) method: 1) transform your data, 2) apply known limits in that domain, 3) inverse transform your data, 4) apply other known limits in that domain, 5) goto 1. It may converge...
