[SciPy-User] frequency components of a signal buried in a noisy time domain signal
Sat Feb 27 07:34:00 CST 2010
On Sat, Feb 27, 2010 at 7:44 AM, David Cournapeau <firstname.lastname@example.org> wrote:
> On Sat, Feb 27, 2010 at 6:41 PM, Nils Wagner
> <email@example.com> wrote:
>> On Fri, 26 Feb 2010 16:32:01 -0500
>> Anne Archibald <firstname.lastname@example.org> wrote:
>>> Looking at a periodic signal buried in noise is a
>>> problem, with many techniques for attacking it. You
>>>really need to be
>>> a little more specific about what you want to do. For
>>>example, is your
>>> input signal really a sinusoid, or does it have harmonic
>>> you trying to detect a weak periodic signal or are you
>>> extract the features of a strong periodic signal? Is
>>> exactly periodic, does it have some (deterministic or
>>> or are you looking for the power spectrum of a broadband
>>> If your input data are non-uniformly sampled, everything
>>> difficult (and computationally expensive), but there are
>>> (e.g. the Lomb-Scargle periodogram).
>> Hi Anne,
>> Thank you very much for your hints !
>> BTW, a BSD licensed code for the Lomb-Scargle periodogram
>> is available at
>> I am newbie to signal processing.
>> Is there a good introduction that you can recommend ?
> It depends on what you are looking for, and the depth and time you are
> willing to spend on it. A "down to earth" book is freely available
> If you are interested in manipulating signals with noise, statistical
> signal processing is where to look at, but I am not sure there is any
> good book which does not start with theory there.
>> There are so many books on signal processing. It should
>> cover engineering applications.
>> What makes a signal weak/strong periodic ?
>> The signals come from real-life application (pressure /
>> acceleration data).
>> Do I need a filter before I apply FFT ?
>> What would you do if you know nothing about the origin of
>> the signal ?
> Well, if you know really nothing, there is nothing you can do :) For
> spectral estimation, there are broadly two kind of methods: parametric
> and non parametric. Unfortunately, scipy itself does not have much
> here - I started the talkbox scikits to implement at least what is
> available in matlab and more for spectral estimation, but I am not
> sure I will have time to work on it much anymore.
> Basic methods, like AR modelling, are quite simple to implement by
> yourself, though.
nitime also has many functions for frequency domain analysis
and there are also a few functions in matplotlib
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