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			108 lines
		
	
	
		
			4.1 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			108 lines
		
	
	
		
			4.1 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
| KISS FFT - A mixed-radix Fast Fourier Transform based up on the principle, 
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| "Keep It Simple, Stupid."
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| 
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|     There are many great fft libraries already around.  Kiss FFT is not trying
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| to be better than any of them.  It only attempts to be a reasonably efficient, 
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| moderately useful FFT that can use fixed or floating data types and can be 
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| incorporated into someone's C program in a few minutes with trivial licensing.
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| 
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| USAGE:
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| 
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|     The basic usage for 1-d complex FFT is:
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| 
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|         #include "kiss_fft.h"
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| 
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|         void * cfg = kiss_fft_alloc( nfft ,inverse_fft );
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| 
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|         while ...
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|         
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|             ... // put kth sample in cx_in[k].r and cx_in[k].i
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|             
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|             kiss_fft( cfg , cx_in , cx_out );
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|             
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|             ... // transformed. DC is in cx_out[0].r and cx_out[0].i 
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|             
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|         free(cfg);
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| 
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|     Note: frequency-domain data is stored from dc up to 2pi.
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|     so cx_out[0] is the dc bin of the FFT
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|     and cx_out[nfft/2] is the Nyquist bin (if exists)
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| 
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|     Declarations are in "kiss_fft.h", along with a brief description of the 
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| functions you'll need to use. 
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| 
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| Code definitions for 1d complex FFTs are in kiss_fft.c.
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| 
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| You can do other cool stuff with the extras you'll find in tools/
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| 
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|     * arbitrary dimension FFTs (complex only currently, apologies to Steve DeKorte -- mebbe next time )
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|     * real FFTs
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|     * fast convolution filtering
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| 
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| The core fft and most tools/ code can be compiled to use float, double 
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| or 16bit short samples. The default is float.
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| 
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| 
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| BACKGROUND:
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| 
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|     I started coding this because I couldn't find a fixed point FFT that didn't 
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| use assembly code.  I started with floating point numbers so I could get the 
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| theory straight before working on fixed point issues.  In the end, I had a 
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| little bit of code that could be recompiled easily to do ffts with short, float
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| or double (other types should be easy too).  
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| 
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|     Once I got my FFT working, I was curious about the speed compared to
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| a well respected and highly optimized fft library.  I don't want to criticize 
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| this great library, so let's call it FFT_BRANDX.
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| During this process, I learned:
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| 
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|     1. FFT_BRANDX has more than 100K lines of code. The core  of kiss_fft is about 500 lines (cpx 1-d).
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|     2. It took me an embarrassingly long time to get FFT_BRANDX working.
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|     3. A simple program using FFT_BRANDX is 522KB. A similar program using kiss_fft is 18KB.
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|     4. FFT_BRANDX is roughly twice as fast as KISS FFT.
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| 
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|     It is wonderful that free, highly optimized libraries like FFT_BRANDX exist.
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| But such libraries carry a huge burden of complexity necessary to extract every 
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| last bit of performance.
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| 
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|     Sometimes simpler is better, even if it's not better.
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| 
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| PERFORMANCE:
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|     (on Athlon XP 2100+, with gcc 2.96, optimization O3, float data type)
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| 
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|     Kiss performed 1000 1024-pt cpx ffts in 100 ms of cpu time.
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|     For comparison, it took md5sum 160ms cputime to process the same amount of data
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| 
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|     Transforming 5 minutes of CD quality audio takes about 1 second (nfft=1024). 
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| 
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| DO NOT:
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|     ... use Kiss if you need the Fastest Fourier Transform in the World
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|     ... ask me to add features that will bloat the code
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| 
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| UNDER THE HOOD:
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| 
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|     Kiss FFT uses a time decimation, mixed-radix, out-of-place FFT. 
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| No scaling is done.  Optimized butterflies are used for factors 2,3,4, and 5. 
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| 
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|     The real optimization code only works for even length ffts.  It does two half-length
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|     FFTs in parallel (packed into real&imag) then twiddles.
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| 
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|     The fast convolution filtering uses the overlap-scrap method, slightly 
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|     modified to put the scrap at the tail.
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|     
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| 
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| LICENSE:
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|     BSD, see COPYING for details. Basically, "free to use&change, give credit where due, no guarantees"
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| 
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| TODO:
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|     *) Add real optimization for odd length FFTs (DST?)
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|     *) Add real optimization to the n-dimensional FFT
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|     *) Add simple windowing function, e.g. Hamming : w(i)=.54-.46*cos(2pi*i/(n-1))
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|     *) Make the fixed point scaling and bit shifts more easily configurable.
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|     *) Document/revisit the input/output fft scaling
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|     *) See if the fixed point code can be optimized a little without adding complexity.
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| 
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| AUTHOR:
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|     Mark Borgerding
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|     Mark@Borgerding.net
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