Talk:Spectral density estimation
|WikiProject Electrical engineering||(Rated Start-class, Mid-importance)|
|WikiProject Statistics||(Rated Start-class, Low-importance)|
I think the added text on the spectral plot has several problems. First, it does not really explain what a spectral plot is. Also, since this article is a general overview of spectral estimation techniques, perhaps the contents of of spectral plot section should be moved to a separate article. Or perhaps it should be merged with periodogram, since, according to , a spectral plot is "essentially a smoothed periodogram". --Zvika (talk) 07:26, 25 February 2008 (UTC)
- Per the above, I have moved the information about spectral plots into periodogram, and spectral plot from a redirect to spectral density estimation to a redirect to periodogram. --Zvika (talk) 11:03, 4 April 2008 (UTC)
Power Spectrum vs Power Spectral Density
I am trying find the difference between 'power spectrum' and 'power spectral densitity'. What I came up with so far is that the spectrum is related to deterministic signals or functions whereas the spectral density concerns stochastic signals. The units of both are different as well. This article states that both are equal, which is to my current knowledge not true. Wilcolalala (talk) 12:53, 7 August 2009 (UTC)
Our coverage of power spectrum estimation (spectral density estimation) seems very weak, with very little bringing together or contrasting the different methods, or giving historical perspective. Indeed, most articles looking for a signal processing treatment of the subject appear to have been being redirected to spectrum analyzer, about a hardware box with very little discussion of algorithms usually used in pure-software methods.
In particular, there is very little discussion of all-poles versus all-zeros methods. There appears to be nothing at all about John Parker Burg or the Burg algorithm. We have quite a detailed article on the Levinson recursion, but nothing to say that this is perhaps its most important application. Linear predictive coding appears to exist in a silo of its own, without even a link to ARMA modelling; while in turn the article on ARMA models doesn't appear to mention power spectrum estimation at all. Autoregressive model is a bit better, but doesn't give any sense how a pure AR fit is likely to compare to other fits.
This is very poor. Given the importance of this topic in signal processing and applications, we ought to be able to match at minimum the level of discussion in Numerical Recipes at least. But at the moment we're way short. Anybody out there willing to step up to the plate? Jheald (talk) 09:23, 26 May 2012 (UTC) pasted here by TSchwenn (talk) 17:08, 26 May 2012 (UTC)
- You forgot to mention that spectral density has a very short section on estimation that doesn't particularly overlap with spectral density estimation in terms of specific methods mentioned. There is also Maximum entropy spectral estimation, which is incomplete, but Burg's algorithm is often described by that terminology. Further Multiple signal classification, again incomplete in terms of references, mentions several methods including Burgs's and others I don't recognise from the names (presumably the originators'). Melcombe (talk) 11:57, 26 May 2012 (UTC) pasted here from WT:WPMATH by TSchwenn (talk) 17:15, 26 May 2012 (UTC)
Dr. Crato's comment on this article
Dr. Crato has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:
I think this article is developed from the sole point of view of signal processing, which is a bit restrictive. It is essentially a question of language, but restrictive anyhow. The general framework is the one of time series analysis or stationary stochastic processes. Unless we consider (white) noise as a signal, the article misses the fact that noise also has a spectral density function.
The article should start by defining second-order stationary processes, the framework under which this analysis is usually performed, although it can be extended. The article is constructed from the "tool perspective".
We hope Wikipedians on this talk page can take advantage of these comments and improve the quality of the article accordingly.
Dr. Crato has published scholarly research which seems to be relevant to this Wikipedia article:
- Reference : Jorge Caiado & Nuno Crato, 2009. "Identifying common dynamic features in stock returns," CEMAPRE Working Papers 0902, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.