Talk:Digital signal processing

FIR vs IIR
The distinction between FIR and IIR is not recursion. for example, y[n] = y[n-1] + x[n] - x[n-k] is not an IIR filter as it could be rewritten as the sum of the last k samples. The actual distinction is based on the number of non-zero outputs for a non-zero input.

And also, the ideal lowpass "sinc" filter is an IIR filter, though it is commonly written using only inputs samples.

— Preceding unsigned comment added by 24.130.9.71 (talk) 21:20, 9 September 2012 (UTC)


 * In your example filter, the output can be nonzero forever with no input. It is not what is usually called an FIR filter, nor what is usually called stable.  I reverted your edit to the article; I believe it is correct as stated. Dicklyon (talk) 04:12, 10 September 2012 (UTC)


 * There is a more important difference between y[n] = y[n-1] + x[n] - x[n-k] and  y[n] = x[n] + x[n-1] + ... + x[n-k+1].
 * With floating-point arithmetic, the recursive implementation has arithmetic error accumulation (random-walk) as n increases. The FIR implementation does not.
 * --Bob K (talk) 13:47, 18 September 2016 (UTC)

Intro reads like an app note, not a text book

 * The goal of DSP is usually to measure, filter and/or compress continuous real-world analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling and then digitizing it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even if this process is more complex than analog processing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.

I think this paragraph shows evidence of making sense, but doesn't. This should stick to first princples (sampling and quantization), or discuss what could be described as "degrees of freedom over domains of operation" (the group of all applications).

As it is, it's been hacked to one odd class of applications. On a music CDROM it's called "ADA". My analog outputs are usually PWM. JohnPritchard (talk) 16:16, 2 January 2013 (UTC)

Don't Talk - Write.
This article has languished for too long. Complaining about things you dislike will not make it better. Start writing. I will begin on the lead - which sucks btw. Codwiki (talk) 16:19, 7 July 2015 (UTC)

Time and space domains
It seems as though most of the content of this section is about Digital filter, not Time domain. I think I could find a place for the material in Digital filter and then summarize Time domain in this section and also add something about space domain for digital imaging processing. I'm happy to do this WP:BOLDly; not looking for permission. Mostly posting here as a reminder to myself. But, if anyone has any objections or suggestions, feel free to respond. ~Kvng (talk) 16:30, 13 February 2018 (UTC)

✅ ~Kvng (talk) 20:39, 24 August 2020 (UTC)

FIR filters have many advantages, but are computationally more demanding
changed FIR to IIR and I had to look carefully to recognise this was wrong. I removed the whole sentence because the section is about Z-plane analysis, not computational efficiency and the sentence does not explain the advantages of an FIR filter. reverted to the original. I beleive these facts are already covered in Finite impulse response and Infinite impulse response and teasing them in this section is unnecessary and appears to be causing some confusion. ~Kvng (talk) 15:45, 26 March 2019 (UTC)
 * OK with me if you take it out after considering the correction I reverted to. Dicklyon (talk) 02:42, 27 March 2019 (UTC)
 * Removed ~Kvng (talk) 14:08, 30 March 2019 (UTC)