Here is some code I use to perform FFT in iOS using Accelerate Framework, which makes it quite fast.
//keep all internal stuff inside this struct
typedef struct FFTHelperRef {
FFTSetup fftSetup; // Accelerate opaque type that contains setup information for a given FFT transform.
COMPLEX_SPLIT complexA; // Accelerate type for complex number
Float32 *outFFTData; // Your fft output data
Float32 *invertedCheckData; // This thing is to verify correctness of output. Compare it with input.
} FFTHelperRef;
//first - initialize your FFTHelperRef with this function.
FFTHelperRef * FFTHelperCreate(long numberOfSamples) {
FFTHelperRef *helperRef = (FFTHelperRef*) malloc(sizeof(FFTHelperRef));
vDSP_Length log2n = log2f(numberOfSamples);
helperRef->fftSetup = vDSP_create_fftsetup(log2n, FFT_RADIX2);
int nOver2 = numberOfSamples/2;
helperRef->complexA.realp = (Float32*) malloc(nOver2*sizeof(Float32) );
helperRef->complexA.imagp = (Float32*) malloc(nOver2*sizeof(Float32) );
helperRef->outFFTData = (Float32 *) malloc(nOver2*sizeof(Float32) );
memset(helperRef->outFFTData, 0, nOver2*sizeof(Float32) );
helperRef->invertedCheckData = (Float32*) malloc(numberOfSamples*sizeof(Float32) );
return helperRef;
}
//pass initialized FFTHelperRef, data and data size here. Return FFT data with numSamples/2 size.
Float32 * computeFFT(FFTHelperRef *fftHelperRef, Float32 *timeDomainData, long numSamples) {
vDSP_Length log2n = log2f(numSamples);
Float32 mFFTNormFactor = 1.0/(2*numSamples);
//Convert float array of reals samples to COMPLEX_SPLIT array A
vDSP_ctoz((COMPLEX*)timeDomainData, 2, &(fftHelperRef->complexA), 1, numSamples/2);
//Perform FFT using fftSetup and A
//Results are returned in A
vDSP_fft_zrip(fftHelperRef->fftSetup, &(fftHelperRef->complexA), 1, log2n, FFT_FORWARD);
//scale fft
vDSP_vsmul(fftHelperRef->complexA.realp, 1, &mFFTNormFactor, fftHelperRef->complexA.realp, 1, numSamples/2);
vDSP_vsmul(fftHelperRef->complexA.imagp, 1, &mFFTNormFactor, fftHelperRef->complexA.imagp, 1, numSamples/2);
vDSP_zvmags(&(fftHelperRef->complexA), 1, fftHelperRef->outFFTData, 1, numSamples/2);
//to check everything =============================
vDSP_fft_zrip(fftHelperRef->fftSetup, &(fftHelperRef->complexA), 1, log2n, FFT_INVERSE);
vDSP_ztoc( &(fftHelperRef->complexA), 1, (COMPLEX *) fftHelperRef->invertedCheckData , 2, numSamples/2);
//=================================================
return fftHelperRef->outFFTData;
}
Use it like this:
Initialize it: FFTHelperCreate(TimeDomainDataLenght);
Pass Float32 time domain data, get frequency domain data on return: Float32 *fftData = computeFFT(fftHelper, buffer, frameSize);
Now you have an array where indexes=frequencies, values=magnitude (squared magnitudes?).
According to Nyquist theorem your maximum possible frequency in that array is half of your sample rate. That is if your sample rate = 44100, maximum frequency you can encode is 22050 Hz.
So go find that Nyquist max frequency for your sample rate: const Float32 NyquistMaxFreq = SAMPLE_RATE/2.0;
Finding Hz is easy: Float32 hz = ((Float32)someIndex / (Float32)fftDataSize) * NyquistMaxFreq;
(fftDataSize = frameSize/2.0)
This works for me. If I generate specific frequency in Audacity and play it - this code detects the right one (the strongest one, you also need to find max in fftData to do this).
(there's still a little mismatch in about 1-2%. not sure why this happens. If someone can explain me why - that would be much appreciated.)
EDIT:
That mismatch happens because pieces I use to FFT are too small. Using larger chunks of time domain data (16384 frames) solves the problem.
This questions explains it:
Unable to get correct frequency value on iphone
EDIT:
Here is the example project: https://github.com/krafter/DetectingAudioFrequency