I band stop filter cuts a frequency band, with the usual cut off control deciding the grequency, and the resonance controls how wide the band is, and I think formant filters emulate vowel sounds or something like that, but could be totally wrong...
Is i was typing the band stop seemes obvious but wanted to clarify.
Still curious about FFT components though as I have started creating plugins with SonicBirth. I have only been using computers for 2 years(gardener by trade) and there is a lot of stuff that goes completely over my head.
I was pretty much pushing my technological limits with the band stop explanation... noooo idea what FFT is... by the way posted a different version of my recent tune up if you want to listen... hope it's a little better! What kind of plug ins are you trying to make?
Anything and everything. SonicBirth alows you to create whatever you want(within boundries). I have made a few different filter plugins but there are so many bits to use I am just trying to find out what bits do or are for.
fast fourier transform i think is about turning waveforms (time based) into spectral stuff (frequency based) because DSPs can do a lot more with that that just with the pure waveforms. it comes from the scientist called fourier, who was the first person to suggest that all sounds can be thought of as being made up of loads of single frequencies. which is where additive synthesis and spectrum analysers and stuff come from. but im buggered if i can understand the science of it.
formant filters are a bit easier. they are just a group of band pass filters with specific band-width, centre frequency, and amplitude settings, which you can insert over a signal to give it a particular character. usually used for emulating vowel sounds, which are defined by particular prominetn frequency bands. same kind of principle as the second half of a vocoder. i did a load of work on that when i was in college, its really interesting if you get into it.
As Double Helix said, FFT converts time domain to frequency domain. In other words, a small chunk of waveform is converted into a *linear* spectral graph, so there are some important implications to bear in mind. An example will probably make more sense than dry statements (feel free to look away now ):
A decent FFT-based filter offers a graph and some kind of resolution setting. The graph is easy, it's what you'd like the filter to do to your audio. How well it will do that, however, depends on the resolution setting *and* the sampling rate - together, they determine the result.
Assuming 44.1 kHz sampling rate and a fairly typical resolution of 4096 (which is really the number of samples, the length of an audio chunk converted as a unit - usually a power of two for fastest processing), the following will apply:
- the frequency resolution will be around 10.8 Hz (4096 frequency bands, from 44100/4096)
- the time window (audio chunk length) will be around 93 ms (4096/44100)
I hope you can see that there is always going to be a trade-off between frequency and time resolution: increasing frequency resolution will necessarily decrease time resolution, meaning you have to choose between precision and smear/lag/delay.
On top of this, since FFT is linear, the lower the frequency you're targetting the higher the resolution you have to use, leading to longer delay at that frequency... eg using 1024 bands, 44100/1024 = ~43 - meaning the very first graph band would cover everything from 0 to 43 Hz, the next 43 to 86 and so on.
On the other hand, FFT is great for weird transforms as well as, say, -3 dB/octave filtering (impossible with regular filters, as each pole is worth -6 dB/octave).
I know this appears heavy duty at first but understanding FFT can only help you decide when to use it