[SciPy-User] frequency components of a signal buried in a noisy time domain signal

[josef.pktd at gmail.com]

On Sat, Feb 27, 2010 at 7:44 AM, David Cournapeau <cournape at gmail.com> wrote:
> On Sat, Feb 27, 2010 at 6:41 PM, Nils Wagner
> <nwagner at iam.uni-stuttgart.de> wrote:
>> On Fri, 26 Feb 2010 16:32:01 -0500
>>  Anne Archibald <peridot.faceted at gmail.com> wrote:
>>> Hi,
>>>
>>> Looking at a periodic signal buried in noise is a
>>>well-studied
>>> 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
>>>content? Are
>>> you trying to detect a weak periodic signal or are you
>>>trying to
>>> extract the features of a strong periodic signal? Is
>>>your signal
>>> exactly periodic, does it have some (deterministic or
>>>random) wander,
>>> or are you looking for the power spectrum of a broadband
>>>signal?
>>>
>>> If your input data are non-uniformly sampled, everything
>>>becomes more
>>> difficult (and computationally expensive), but there are
>>>solutions
>>> (e.g. the Lomb-Scargle periodogram).
>>>
>>> Anne
>>>
>> Hi Anne,
>>
>> Thank you very much for your hints !
>>
>> BTW, a BSD licensed code for the Lomb-Scargle periodogram
>> is available at
>> http://www.mathworks.com/matlabcentral/fileexchange/993-lombscargle-m
>> http://www.mathworks.com/matlabcentral/fileexchange/20004-lomb-lomb-scargle-periodogram
>>
>>
>> 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
> here:
>
> http://www.dspguide.com
>
> 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

Josef

>
> David
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