[SciPy-User] Signal to noise ratio
Wed Feb 24 09:23:11 CST 2010
On Wed, Feb 24, 2010 at 10:16 AM, Ivo Maljevic <email@example.com> wrote:
> 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.
Note: I linked to #Statistical_definition not the top of the wikipedia page
and I checked the source in scipy.stats:
Calculates the signal-to-noise ratio, defined as the ratio between the mean
and the standard deviation.
m = np.mean(a, axis)
sd = samplestd(a, axis)
return np.where(sd == 0, 0, m/sd)
I didn't know about the different definitions until I read the
Wikipedia page, but that's what's currently in scipy.stats
> 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:
> 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, <firstname.lastname@example.org> wrote:
>> On Wed, Feb 24, 2010 at 8:12 AM, Nils Wagner
>> <email@example.com> 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
>> 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.
>> > Nils
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