[SciPy-User] frequency components of a signal buried in a noisy time domain signal
Sat Feb 27 04:54:27 CST 2010
> Hi Ivo,
> Thank you very much for your hints.
> My experience is, that spectrograms work well with
> synthetic signals.
What is needed to use spectrograms for real-life signals ?
The same way the frequency content of your synthetic signal changes over
time, it happens with real signal (e.g., voice,
communication system's signal, etc).
I mean do I need a filter etc. ?
There are several levels of filtering:
1. You want to filter a signal even before you sample it (e.g., in comm
systems, you want to remove as much noise+interference) and focus only on
the band that interests you, plus you want to satisfy Nyquist.
2. After you have sampled your signal (if it was analog to begin with,
otherwise, skip step 1), you may do additional digital filtering, to further
reduce the impact of noise/interference, but this is all application
3. Depending on what you are doing, you might want to consider windowing
techniques in your spectral analysis, if you want to suppress the sidelobes
(fft uses rectangular window, which has high sidelobes - sinc function), or
to better identify the peaks. It all comes down to trade-offs.
In any case, to think about these problems, you need to go over signal
processing material first.
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