[SciPy-User] Signal to noise ratio
Ivo Maljevic
ivo.maljevic@gmail....
Wed Feb 24 09:16:19 CST 2010
One would think that you can always rely on wikipedia when it comes to math
and engineering, but it seems that is not tha case.
Josef, In the page you referenced, the SNR, or signal to noise ratio, is
defined as the ratio between the signal and noise powers. Consequently, in
terms of signals and standard deviations, it is defined as a ratio of the
average signal power and the noise variance (NOT its squre root, or standard
deviation). Or:
SNR = P_s / sigma^2
where P_s is the average signal power, and the noise variance is used to
measure the noise power. The assumption here is that the noise is a zero
mean process, otherwise variance and power wouldn't be the same thing.
Nils, your question is way too generic for anyone to help you directly. I
can only point to you that your signal to noise ratio is quite low:
>>> 10*math.log10(0.0447)
-13.496924768680636
Maybe your signal is to narrow compared to the overal band you are working
with (or you have DS spread spectrum signal?).
Anyway, you will need to figure out which filter you want to use (e.g.,
butterworth for maximally flat characteristic in the passband, etc).
On 24 February 2010 09:33, <josef.pktd@gmail.com> wrote:
> On Wed, Feb 24, 2010 at 8:12 AM, Nils Wagner
> <nwagner@iam.uni-stuttgart.de> wrote:
> > Hi all,
> >
> > I have two questions concerning signal processing
> >
> > I have used scipy.stats.signaltonoise to compute the
> > signal-to-noise ratio.
> > The value is 0.0447.
> > How can I judge it ?
>
> It's just mean over standard deviation
> http://en.wikipedia.org/wiki/Signal-to-noise_ratio#Statistical_definition
>
> I never use it, but the interpretation will depend on what your
> level/mean/expected_value means.
>
> >
> > How can I filter out high frequencies using scipy ?
> > How can I eliminate noise from the signal ?
>
> (I'm no help here) There are many prefabricated filters in
> scipy.signal, but I only use lfilter.
>
> Josef
>
> >
> > Nils
> > _______________________________________________
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> >
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