Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!uwm.edu!gem.mps.ohio-state.edu!ginosko!uunet!microsoft!brianw From: brianw@microsoft.UUCP (Brian Willoughby) Newsgroups: comp.dsp Subject: Real-time Fourier Transform Keywords: FFT DFT Message-ID: <7899@microsoft.UUCP> Date: 29 Sep 89 22:07:54 GMT Reply-To: brianw@microsoft.UUCP (Brian Willoughby) Organization: Microsoft Corp., Redmond WA Lines: 30 In article <3441@abaa.UUCP> esker@abaa.UUCP (Lawrence Esker) writes: [...] >Yes, the sliding-FFT looked like a great design invention until you studied >it more closely and realized it was simply the original Discrete Fourier >Transform (DFT) restated in a different way. Since the FFT is an algorithmic >shortcut to the DFT, it made me chuckle to see the DFT used to perform the >FFT, albeit under a new name of sliding-FFT. You never stated whether you thought the DFT could be done in real time. The "sliding-FFT" (whatever you wish to call it) seemed simple enough (in terms of how many math computations per sample) for converting real-time audio into the frequency domain. But it does appear that computing an additional inverse FT under the same time constraints would not be possible. Its been a while since I studied this in college, anyone care to describe the DFT vs. FFT in layman's terms, or point to a text which does? I seem to remember that the inverse FT is basically the same operation as the FT from time- to frequency-domain... One use for real-time FT would be a digital spectrum analyser with say 256 points instead of the usual 10 band versions available with analog units. Gotta have some blinky light algorithms for my DSP project board to execute :-) Brian Willoughby UUCP: ...!{tikal, sun, uunet, elwood}!microsoft!brianw InterNet: microsoft!brianw@uunet.UU.NET or: microsoft!brianw@Sun.COM Bitnet brianw@microsoft.UUCP