I was looking for a simple arduino fft library to create an equalizer.

I found lots of assembler code or 16 or 32 bit versions but nothing that was simple and out of the box usable.

I have modified the fix_fft.c to use 8bit values.

here is it for everyone with the same problem.

I did not test the inverse fft. I guess there is a bug with the scaling.

fix_fft.h

#ifndef FIXFFT_H

#define FIXFFT_H

#include <WProgram.h>

/*

fix_fft() - perform forward/inverse fast Fourier transform.

fr[n],fi[n] are real and imaginary arrays, both INPUT AND

RESULT (in-place FFT), with 0 <= n < 2**m; set inverse to

0 for forward transform (FFT), or 1 for iFFT.

*/

int fix_fft(char fr[], char fi[], int m, int inverse);

/*

fix_fftr() - forward/inverse FFT on array of real numbers.

Real FFT/iFFT using half-size complex FFT by distributing

even/odd samples into real/imaginary arrays respectively.

In order to save data space (i.e. to avoid two arrays, one

for real, one for imaginary samples), we proceed in the

following two steps: a) samples are rearranged in the real

array so that all even samples are in places 0-(N/2-1) and

all imaginary samples in places (N/2)-(N-1), and b) fix_fft

is called with fr and fi pointing to index 0 and index N/2

respectively in the original array. The above guarantees

that fix_fft "sees" consecutive real samples as alternating

real and imaginary samples in the complex array.

*/

int fix_fftr(char f[], int m, int inverse);

#endif

fix_fft.cpp

`#include <avr/pgmspace.h>`

#include "fix_fft.h"

#include <WProgram.h>

/* fix_fft.c - Fixed-point in-place Fast Fourier Transform */

/*

All data are fixed-point short integers, in which -32768

to +32768 represent -1.0 to +1.0 respectively. Integer

arithmetic is used for speed, instead of the more natural

floating-point.

For the forward FFT (time -> freq), fixed scaling is

performed to prevent arithmetic overflow, and to map a 0dB

sine/cosine wave (i.e. amplitude = 32767) to two -6dB freq

coefficients. The return value is always 0.

For the inverse FFT (freq -> time), fixed scaling cannot be

done, as two 0dB coefficients would sum to a peak amplitude

of 64K, overflowing the 32k range of the fixed-point integers.

Thus, the fix_fft() routine performs variable scaling, and

returns a value which is the number of bits LEFT by which

the output must be shifted to get the actual amplitude

(i.e. if fix_fft() returns 3, each value of fr[] and fi[]

must be multiplied by 8 (2**3) for proper scaling.

Clearly, this cannot be done within fixed-point short

integers. In practice, if the result is to be used as a

filter, the scale_shift can usually be ignored, as the

result will be approximately correctly normalized as is.

Written by: Tom Roberts 11/8/89

Made portable: Malcolm Slaney 12/15/94 malcolm@interval.com

Enhanced: Dimitrios P. Bouras 14 Jun 2006 dbouras@ieee.org

Modified for 8bit values David Keller 10.10.2010

*/

#define N_WAVE 256 /* full length of Sinewave[] */

#define LOG2_N_WAVE 8 /* log2(N_WAVE) */

/*

Since we only use 3/4 of N_WAVE, we define only

this many samples, in order to conserve data space.

*/

const prog_int8_t Sinewave[N_WAVE-N_WAVE/4] PROGMEM = {

0, 3, 6, 9, 12, 15, 18, 21,

24, 28, 31, 34, 37, 40, 43, 46,

48, 51, 54, 57, 60, 63, 65, 68,

71, 73, 76, 78, 81, 83, 85, 88,

90, 92, 94, 96, 98, 100, 102, 104,

106, 108, 109, 111, 112, 114, 115, 117,

118, 119, 120, 121, 122, 123, 124, 124,

125, 126, 126, 127, 127, 127, 127, 127,

127, 127, 127, 127, 127, 127, 126, 126,

125, 124, 124, 123, 122, 121, 120, 119,

118, 117, 115, 114, 112, 111, 109, 108,

106, 104, 102, 100, 98, 96, 94, 92,

90, 88, 85, 83, 81, 78, 76, 73,

71, 68, 65, 63, 60, 57, 54, 51,

48, 46, 43, 40, 37, 34, 31, 28,

24, 21, 18, 15, 12, 9, 6, 3,

0, -3, -6, -9, -12, -15, -18, -21,

-24, -28, -31, -34, -37, -40, -43, -46,

-48, -51, -54, -57, -60, -63, -65, -68,

-71, -73, -76, -78, -81, -83, -85, -88,

-90, -92, -94, -96, -98, -100, -102, -104,

-106, -108, -109, -111, -112, -114, -115, -117,

-118, -119, -120, -121, -122, -123, -124, -124,

-125, -126, -126, -127, -127, -127, -127, -127,

/*-127, -127, -127, -127, -127, -127, -126, -126,

-125, -124, -124, -123, -122, -121, -120, -119,

-118, -117, -115, -114, -112, -111, -109, -108,

-106, -104, -102, -100, -98, -96, -94, -92,

-90, -88, -85, -83, -81, -78, -76, -73,

-71, -68, -65, -63, -60, -57, -54, -51,

-48, -46, -43, -40, -37, -34, -31, -28,

-24, -21, -18, -15, -12, -9, -6, -3, */

};

/*

FIX_MPY() - fixed-point multiplication & scaling.

Substitute inline assembly for hardware-specific

optimization suited to a particluar DSP processor.

Scaling ensures that result remains 16-bit.

*/

inline char FIX_MPY(char a, char b)

{

//Serial.println(a);

//Serial.println(b);

/* shift right one less bit (i.e. 15-1) */

int c = ((int)a * (int)b) >> 6;

/* last bit shifted out = rounding-bit */

b = c & 0x01;

/* last shift + rounding bit */

a = (c >> 1) + b;

/*

Serial.println(Sinewave[3]);

Serial.println(c);

Serial.println(a);

while(1);*/

return a;

}

/*

fix_fft() - perform forward/inverse fast Fourier transform.

fr[n],fi[n] are real and imaginary arrays, both INPUT AND

RESULT (in-place FFT), with 0 <= n < 2**m; set inverse to

0 for forward transform (FFT), or 1 for iFFT.

*/

int fix_fft(char fr[], char fi[], int m, int inverse)

{

int mr, nn, i, j, l, k, istep, n, scale, shift;

char qr, qi, tr, ti, wr, wi;

n = 1 << m;

/* max FFT size = N_WAVE */

if (n > N_WAVE)

return -1;

mr = 0;

nn = n - 1;

scale = 0;

/* decimation in time - re-order data */

for (m=1; m<=nn; ++m) {

l = n;

do {

l >>= 1;

} while (mr+l > nn);

mr = (mr & (l-1)) + l;

if (mr <= m)

continue;

tr = fr[m];

fr[m] = fr[mr];

fr[mr] = tr;

ti = fi[m];

fi[m] = fi[mr];

fi[mr] = ti;

}

l = 1;

k = LOG2_N_WAVE-1;

while (l < n) {

if (inverse) {

/* variable scaling, depending upon data */

shift = 0;

for (i=0; i<n; ++i) {

j = fr[i];

if (j < 0)

j = -j;

m = fi[i];

if (m < 0)

m = -m;

if (j > 16383 || m > 16383) {

shift = 1;

break;

}

}

if (shift)

++scale;

} else {

/*

fixed scaling, for proper normalization --

there will be log2(n) passes, so this results

in an overall factor of 1/n, distributed to

maximize arithmetic accuracy.

*/

shift = 1;

}

/*

it may not be obvious, but the shift will be

performed on each data point exactly once,

during this pass.

*/

istep = l << 1;

for (m=0; m<l; ++m) {

j = m << k;

/* 0 <= j < N_WAVE/2 */

wr = pgm_read_word_near(Sinewave + j+N_WAVE/4);

/*Serial.println("asdfasdf");

Serial.println(wr);

Serial.println(j+N_WAVE/4);

Serial.println(Sinewave[256]);

Serial.println("");*/

wi = -pgm_read_word_near(Sinewave + j);

if (inverse)

wi = -wi;

if (shift) {

wr >>= 1;

wi >>= 1;

}

for (i=m; i<n; i+=istep) {

j = i + l;

tr = FIX_MPY(wr,fr[j]) - FIX_MPY(wi,fi[j]);

ti = FIX_MPY(wr,fi[j]) + FIX_MPY(wi,fr[j]);

qr = fr[i];

qi = fi[i];

if (shift) {

qr >>= 1;

qi >>= 1;

}

fr[j] = qr - tr;

fi[j] = qi - ti;

fr[i] = qr + tr;

fi[i] = qi + ti;

}

}

--k;

l = istep;

}

return scale;

}

/*

fix_fftr() - forward/inverse FFT on array of real numbers.

Real FFT/iFFT using half-size complex FFT by distributing

even/odd samples into real/imaginary arrays respectively.

In order to save data space (i.e. to avoid two arrays, one

for real, one for imaginary samples), we proceed in the

following two steps: a) samples are rearranged in the real

array so that all even samples are in places 0-(N/2-1) and

all imaginary samples in places (N/2)-(N-1), and b) fix_fft

is called with fr and fi pointing to index 0 and index N/2

respectively in the original array. The above guarantees

that fix_fft "sees" consecutive real samples as alternating

real and imaginary samples in the complex array.

*/

int fix_fftr(char f[], int m, int inverse)

{

int i, N = 1<<(m-1), scale = 0;

char tt, *fr=f, *fi=&f[N];

if (inverse)

scale = fix_fft(fi, fr, m-1, inverse);

for (i=1; i<N; i+=2) {

tt = f[N+i-1];

f[N+i-1] = f[i];

f[i] = tt;

}

if (! inverse)

scale = fix_fft(fi, fr, m-1, inverse);

return scale;

}