What would be the best way to get Hz frequency value from audio stream(music) on iOS? What are the best and easiest frameworks provided by Apple to do that. Thanks in advance.
"Audio Frequency Analyzer" is a powerful real-time audio analyzer (RTA) app for iPhone. This app can measure the frequency response in 30 split band scale of 1/3 octave in real time.
Sound Meter Pro: Sound Meter PRO works with Android phones to measure sound or decibel level. It can also measure sound pressure.
The native sampling frequency of iOS devices is 44100 Hz, while Android devices can operate at either 8, 16, 24, 44.1 or 48 kHz.
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
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