I've read these question:
Using the Apple FFT and Accelerate Framework
How do I set up a buffer when doing an FFT using the Accelerate framework?
iOS FFT Accerelate.framework draw spectrum during playback
They all describe how to setup fft with the accelerate framework. With their help I was able to setup fft and get a basic spectrum analyzer. Right now, I'm displaying all values I got from the fft. However, I only want to show 10-15, or a variable number, of bars respreseting certain frequencies. Just like the iTunes or WinAmp Level Meter.
1. Do I need to average magnitude values from a range of frequencies? Or do they just show you a magnitude for the specific frequency bar?
2. Also, do I need to convert my magnitude values to db?
3. How do I map my data to a certain range. Do I map against the max db range for my sounds bitdepth? Getting the max Value for a bin will lead to jumping max mapping values.
My RenderCallback:
static OSStatus PlaybackCallback(void *inRefCon,
AudioUnitRenderActionFlags *ioActionFlags,
const AudioTimeStamp *inTimeStamp,
UInt32 inBusNumber,
UInt32 inNumberFrames,
AudioBufferList *ioData)
{
UInt32 maxSamples = kAudioBufferNumFrames;
UInt32 log2n = log2f(maxSamples); //bins
UInt32 n = 1 << log2n;
UInt32 stride = 1;
UInt32 nOver2 = n/2;
COMPLEX_SPLIT A;
float *originalReal, *obtainedReal, *frequencyArray, *window, *in_real;
in_real = (float *) malloc(maxSamples * sizeof(float));
A.realp = (float *) malloc(nOver2 * sizeof(float));
A.imagp = (float *) malloc(nOver2 * sizeof(float));
memset(A.imagp, 0, nOver2 * sizeof(float));
obtainedReal = (float *) malloc(n * sizeof(float));
originalReal = (float *) malloc(n * sizeof(float));
frequencyArray = (float *) malloc(n * sizeof(float));
//-- window
UInt32 windowSize = maxSamples;
window = (float *) malloc(windowSize * sizeof(float));
memset(window, 0, windowSize * sizeof(float));
// vDSP_hann_window(window, windowSize, vDSP_HANN_DENORM);
vDSP_blkman_window(window, windowSize, 0);
vDSP_vmul(ioBuffer, 1, window, 1, in_real, 1, maxSamples);
//-- window
vDSP_ctoz((COMPLEX*)in_real, 2, &A, 1, maxSamples/2);
vDSP_fft_zrip(fftSetup, &A, stride, log2n, FFT_FORWARD);
vDSP_fft_zrip(fftSetup, &A, stride, log2n, FFT_INVERSE);
float scale = (float) 1.0 / (2 * n);
vDSP_vsmul(A.realp, 1, &scale, A.realp, 1, nOver2);
vDSP_vsmul(A.imagp, 1, &scale, A.imagp, 1, nOver2);
vDSP_ztoc(&A, 1, (COMPLEX *) obtainedReal, 2, nOver2);
vDSP_zvmags(&A, 1, obtainedReal, 1, nOver2);
Float32 one = 1;
vDSP_vdbcon(obtainedReal, 1, &one, obtainedReal, 1, nOver2, 0);
for (int i = 0; i < nOver2; i++) {
frequencyArray[i] = obtainedReal[i];
}
// Extract the maximum value
double fftMax = 0.0;
vDSP_maxmgvD((double *)obtainedReal, 1, &fftMax, nOver2);
float max = sqrt(fftMax);
}
Playing some music, I get values from -96db to 0db.
Plotting a point at:
CGPointMake(i, kMaxSpectrumHeight * (1 - frequencyArray[i]/-96.));
is giving my a rather rounded curve:
plot1
If I don't convert to db I can plot by multiplying my array value by 10000 and get nice peaks.
plot2
Am I doing something totally wrong? And how do I get to showing a variable number of bars?
Do I need to average magnitude values from a range of frequencies? Or do they just show you a magnitude for the specific frequency bar?
Yes, you definitely need to average values across the bands you've defined. Showing just one FFT bin is madness.
Also, do I need to convert my magnitude values to db?
Yes: dB is a log scale. Not coincidentally, human hearing also works (roughly) on a log scale. The values will therefore look more natural to humans if you take log2() of the values before plotting them.
How do I map my data to a certain range. Do I map against the max db range for my sounds bitdepth? Getting the max Value for a bin will
lead to jumping max mapping values.
I find the easiest thing to do (conceptually at least) is to convert your values from whatever format into a 0..1, i.e. 'normalised and scaled' float value. Then from there you can convert if necessary to something you need to plot. For example
SInt16 rawValue = fft[0]; // let's say this comes back as 12990
float scaledValue = rawValue/32767.; // This is MAX_INT for 16-bit;
// dividing we get .396435438 which is much easier for most people
// to see conceptually as 39% of our max possible value
float displayValue = log2(scaledValue);
my_fft[0] = displayValue;
Related
What is the most efficient way of generating a sine wave for a device running IOS. For the purposes of the exercise assume a frequency of 440Hz and a sampling rate of 44100Hz and 1024 samples.
A vanilla C implementation looks something like.
#define SAMPLES 1024
#define TWO_PI (3.14159 * 2)
#define FREQUENCY 440
#define SAMPLING_RATE 44100
int main(int argc, const char * argv[]) {
float samples[SAMPLES];
float phaseIncrement = TWO_PI * FREQUENCY / SAMPLING_RATE;
float currentPhase = 0.0;
for (int i = 0; i < SAMPLES; i ++){
samples[i] = sin(currentPhase);
currentPhase += phaseIncrement;
}
return 0;
}
To take advantage of the Accelerate Framework and the vecLib vvsinf function the loop can be changed to only do the addition.
#define SAMPLES 1024
#define TWO_PI (3.14159 * 2)
#define FREQUENCY 440
#define SAMPLING_RATE 44100
int main(int argc, const char * argv[]) {
float samples[SAMPLES] __attribute__ ((aligned));
float results[SAMPLES] __attribute__ ((aligned));
float phaseIncrement = TWO_PI * FREQUENCY / SAMPLING_RATE;
float currentPhase = 0.0;
for (int i = 0; i < SAMPLES; i ++){
samples[i] = currentPhase;
currentPhase += phaseIncrement;
}
vvsinf(results, samples, SAMPLES);
return 0;
}
But is just applying the vvsinf function as far as I should go in terms of efficiency?
I don't really understand the Accelerate framework well enough to know if I can also replace the loop. Is there a vecLib or vDSP function I can use?
For that matter is it possible to use an entirely different alogrithm to fill a buffer with a sine wave?
Given that you are computing the sine of a phase argument which increases in fixed increments, it is generally much faster to implement the signal generation with a recurrence equation as described in this "How to Create Oscillators in Software" post and some more in this "DSP Trick: Sinusoidal Tone Generator" post, both on dspguru:
y[n] = 2*cos(w)*y[n-1] - y[n-2]
Note that this recurrence equation can be subject to numerical roundoff error accumulation, you should avoid computing too many samples at a time (your selection of SAMPLES == 1024 should be fine). This recurrence equation can be used after you have obtained the first two values y[0] and y[1] (the initial conditions). Since you are generating with an initial phase of 0, those are simply:
samples[0] = 0;
samples[1] = sin(phaseIncrement);
or more generally with an arbitrary initial phase (particularly useful to reinitialize the recurrence equation every so often to avoid the numerical roundoff error accumulation I mentioned earlier):
samples[0] = sin(initialPhase);
samples[1] = sin(initialPhase+phaseIncrement);
The recurrence equation can then be implemented directly with:
float scale = 2*cos(phaseIncrement);
// initialize first 2 samples for the 0 initial phase case
samples[0] = 0;
samples[1] = sin(phaseIncrement);
for (int i = 2; i < SAMPLES; i ++){
samples[i] = scale * samples[i-1] - samples[i-2];
}
Note that this implementation could be vectorized by computing multiple tones (each with the same frequency, but with larger phase increments between samples) with appropriate relative phase shifts, then interleaving the results to obtain the original tone (e.g. computing sin(4*w*n), sin(4*w*n+w), sin(4*w*n+2*w) and sin(4*w*n+3*w)). This would however make the implementation a lot more obscure, for a relatively small gain.
Alternatively the equation can be implemented by making use of vDsp_deq22:
// setup dummy array which will hold zeros as input
float nullInput[SAMPLES];
memset(nullInput, 0, SAMPLES * sizeof(float));
// setup filter coefficients
float coefficients[5];
coefficients[0] = 0;
coefficients[1] = 0;
coefficients[2] = 0;
coefficients[3] = -2*cos(phaseIncrement);
coefficients[4] = 1.0;
// initialize first 2 samples for the 0 initial phase case
samples[0] = 0;
samples[1] = sin(phaseIncrement);
vDsp_deq22(nullInput, 1, coefficients, samples, 1, SAMPLES-2);
If efficiency is required, you could pre-load a 440hz (44100 / 440) sine waveform look-up table and loop around it without further mapping or pre-load a 1hz (44100 / 44100) sine waveform look-up table and loop around by skipping samples to reach 440hz just as you did by incrementing a phase counter. Using look-up tables should be faster than computing sin().
Method A (using 440hz sine waveform):
#define SAMPLES 1024
#define FREQUENCY 440
#define SAMPLING_RATE 44100
#define WAVEFORM_LENGTH (SAMPLING / FREQUENCY)
int main(int argc, const char * argv[]) {
float waveform[WAVEFORM_LENGTH];
LoadSinWaveForm(waveform);
float samples[SAMPLES] __attribute__ ((aligned));
float results[SAMPLES] __attribute__ ((aligned));
for (int i = 0; i < SAMPLES; i ++){
samples[i] = waveform[i % WAVEFORM_LENGTH];
}
vvsinf(results, samples, SAMPLES);
return 0;
}
Method B (using 1hz sine waveform):
#define SAMPLES 1024
#define FREQUENCY 440
#define TWO_PI (3.14159 * 2)
#define SAMPLING_RATE 44100
#define WAVEFORM_LENGTH SAMPLING_RATE // since it's 1hz
int main(int argc, const char * argv[]) {
float waveform[WAVEFORM_LENGTH];
LoadSinWaveForm(waveform);
float samples[SAMPLES] __attribute__ ((aligned));
float results[SAMPLES] __attribute__ ((aligned));
float phaseIncrement = TWO_PI * FREQUENCY / SAMPLING_RATE;
float currentPhase = 0.0;
for (int i = 0; i < SAMPLES; i ++){
samples[i] = waveform[floor(currentPhase) % WAVEFORM_LENGTH];
currentPhase += phaseIncrement;
}
vvsinf(results, samples, SAMPLES);
return 0;
}
Please note that:
Method A is susceptible to frequency inaccuracy due to assuming that your frequency always divides correctly the sampling rate, which is not true. That means you may get 441hz or 440hz with a glitch.
Method B is susceptible to aliasing as the frequency goes up an gets closer to the Nyquist frequency, but it's a good trade-off between performance, quality and memory consumption if synthesizing reasonable low frequencies such as the one in your example.
I'm trying to do a realtime FFT on microphone data in iOS using Novacaine and I cant seem to pass the FFT setup and ring buffer to my FFT function.
For testing, my plan was to do the setup in the viewWillAppear method, then execute my FFT function when a button is pressed. I'll then deallocate the memory for my buffers and destroy the FFTsetup when the window closes.
Here's what I have so far. I've tried several variations of passing arguments to viewWillAppear but nothing seems to be working. Any suggestions are appreceated.
- (void)viewWillAppear:(BOOL)animated
{
[super viewWillAppear:animated];
ringBuffer = new RingBuffer(8192, 2);
audioManager = [Novocaine audioManager];
[audioManager setInputBlock:^(float *data, UInt32 numFrames, UInt32 numChannels) {
// Setup FFT here
int numSamples = 8192;
// Setup the length
vDSP_Length log2n = log2f(numSamples);
// Calculate the weights array. This is a one-off operation.
FFTSetup fftSetup = vDSP_create_fftsetup(log2n, FFT_RADIX2);
// For an FFT, numSamples must be a power of 2, i.e. is always even
int nOver2 = numSamples/2;
// Populate *window with the values for a hamming window function
float *window = (float *)malloc(sizeof(float) * numSamples);
vDSP_hamm_window(window, numSamples, 0);
// Window the samples
vDSP_vmul(data, 1, window, 1, data, 1, numSamples);
// Define complex buffer
COMPLEX_SPLIT A;
A.realp = (float *) malloc(nOver2*sizeof(float));
A.imagp = (float *) malloc(nOver2*sizeof(float));
}];
}
- (IBAction)buttonPressed:(id)sender {
// do myFFT here
// Pack samples:
// C(re) -> A[n], C(im) -> A[n+1]
vDSP_ctoz((COMPLEX*)data, 2, &A, 1, numSamples/2);
//Perform a forward FFT using fftSetup and A
//Results are returned in A
vDSP_fft_zrip(fftSetup, &A, 1, log2n, FFT_FORWARD);
//Convert COMPLEX_SPLIT A result to magnitudes
float *amp = (float *)malloc(sizeof(float) * numSamples);
amp[0] = A.realp[0]/(numSamples*2);
float max = 0;
int indexOfMax = -1;
for(int i=1; i<numSamples; i++) {
amp[i]=A.realp[i]*A.realp[i]+A.imagp[i]*A.imagp[i];
//printf("i[%ld]: %.1f %ldHz \n", (long)i, amp[i], (long)22000 * i/numSamples);
if(amp[i] > max) {
max = amp[i];
indexOfMax = i;
}
}
long fmax = ((long)indexOfMax - numSamples/2)*44100/4096;
printf("max frequency is %ld\n", fmax);
free(amp);
}
The real-time audio input block isn't for doing any heavy computation, such as an FFT. Instead the input block should just (quickly!) copy the data into a large enough ring buffer.
Later, during a displayLink timer or when the button is pressed, you can check the ring buffer to see if it has enough new data, and then do the FFT.
Your code also seems to confuse the FFT size numSamples with the (usually much smaller) actual amount of samples received by the callback, which is numFrames. You can't do the FFT until enough callbacks have been called for numFrames to sum up to be equal or greater than the FFT size.
I've been trying to get exact frequencies using the FFT in Apple's Accelerate framework, but I'm having trouble working out why my values are off the true frequency.
I have been using this article http://www.dspdimension.com/admin/pitch-shifting-using-the-ft/ as the basis for my implementation, and after really struggling to get to the point I'm at now, I am totally stumped.
So far I've got audio in -> Hanning window -> FFT -> phase calculation -> weird final output. I'd think that there will be a problem with my maths somewhere, but I'm really out of ideas by now.
The outputs are a lot lower what they should be, e.g., I input 440Hz and it prints out 190Hz, or I input 880Hz and it prints out 400Hz. For the most part these results are consistent, but not always, and there doesn't seem to be any common factor between anything either...
Here is my code:
enum
{
sampleRate = 44100,
osamp = 4,
samples = 4096,
range = samples * 7 / 16,
step = samples / osamp
};
NSMutableArray *fftResults;
static COMPLEX_SPLIT A;
static FFTSetup setupReal;
static uint32_t log2n, n, nOver2;
static int32_t stride;
static float expct = 2*M_PI*((double)step/(double)samples);
static float phase1[range];
static float phase2[range];
static float dPhase[range];
- (void)fftSetup
{
// Declaring integers
log2n = 12;
n = 1 << log2n;
stride = 1;
nOver2 = n / 2;
// Allocating memory for complex vectors
A.realp = (float *) malloc(nOver2 * sizeof(float));
A.imagp = (float *) malloc(nOver2 * sizeof(float));
// Allocating memory for FFT
setupReal = vDSP_create_fftsetup(log2n, FFT_RADIX2);
// Setting phase
memset(phase2, 0, range * sizeof(float));
}
// For each sample in buffer...
for (int bufferCount = 0; bufferCount < audioBufferList.mNumberBuffers; bufferCount++)
{
// Declaring samples from audio buffer list
SInt16 *samples = (SInt16*)audioBufferList.mBuffers[bufferCount].mData;
// Creating Hann window function
for (int i = 0; i < nOver2; i++)
{
double hannMultiplier = 0.5 * (1 - cos((2 * M_PI * i) / (nOver2 - 1)));
// Applying window to each sample
A.realp[i] = hannMultiplier * samples[i];
A.imagp[i] = 0;
}
// Applying FFT
vDSP_fft_zrip(setupReal, &A, stride, log2n, FFT_FORWARD);
// Detecting phase
vDSP_zvphas(&A, stride, phase1, stride, range);
// Calculating phase difference
vDSP_vsub(phase2, stride, phase1, stride, dPhase, stride, range);
// Saving phase
memcpy(phase2, phase1, range * sizeof(float));
// Extracting DSP outputs
for (size_t j = 0; j < nOver2; j++)
{
NSNumber *realNumbers = [NSNumber numberWithFloat:A.realp[j]];
NSNumber *imagNumbers = [NSNumber numberWithFloat:A.imagp[j]];
[real addObject:realNumbers];
[imag addObject:imagNumbers];
}
// Combining real and imaginary parts
[resultsCombined addObject:real];
[resultsCombined addObject:imag];
// Filling FFT output array
[fftResults addObject:resultsCombined];
}
}
int fftCount = [fftResults count];
NSLog(#"FFT Count: %d",fftCount);
// For each FFT...
for (int i = 0; i < fftCount; i++)
{
// Declaring integers for peak detection
float peak = 0;
float binNumber = 0;
// Declaring integers for phase detection
float deltaPhase;
static float trueFrequency[range];
for (size_t j = 1; j < range; j++)
{
// Calculating bin magnitiude
float realVal = [[[[fftResults objectAtIndex:i] objectAtIndex:0] objectAtIndex:j] floatValue];
float imagVal = [[[[fftResults objectAtIndex:i] objectAtIndex:1] objectAtIndex:j] floatValue];
float magnitude = sqrtf(realVal*realVal + imagVal*imagVal);
// Peak detection
if (magnitude > peak)
{
peak = magnitude;
binNumber = (float)j;
}
// Getting phase difference
deltaPhase = dPhase[j];
// Subtract expected difference
deltaPhase -= (float)j*expct;
// Map phase difference into +/- pi interval
int qpd = deltaPhase / M_PI;
if (qpd >= 0)
qpd += qpd&1;
else
qpd -= qpd&1;
deltaPhase -= M_PI * (float)qpd;
// Getting bin deviation from +/i interval
float deltaFrequency = osamp * deltaPhase / (2 * M_PI);
// Calculating true frequency at the j-th partial
trueFrequency[j] = (j * (sampleRate/samples)) + (deltaFrequency * (sampleRate/samples));
}
UInt32 mag;
mag = binNumber;
// Extracting frequency at bin peak
float f = trueFrequency[mag];
NSLog(#"True frequency = %fHz", f);
float b = roundf(binNumber*(sampleRate/nOver2));
NSLog(#" Bin frequency = %fHz", b);
}
Note that the expected phase difference (even for a bin-centered frequency) depends on both the window offset or overlap of the FFT pairs, and the bin number or frequency of the FFT result. e.g. If you offset the windows by very little (1 sample), then the unwrapped phase difference between 2 FFTs will be smaller than with a larger offset. At the same offset, if the frequency is higher, the expected phase difference between the same bin of two FFTs will be greater (or it will wrap more).
UPDATE 2016-03-15
Please take a look at this project: https://github.com/ooper-shlab/aurioTouch2.0-Swift. It has been ported to Swift and contains every answer you're looking for, if you cam here.
I did a lot of research and learned a lot about FFT and the Accelerate Framework. But after days of experiments I'm kind of frustrated.
I want to display the frequency spectrum of an audio file during playback in a diagram. For every time interval it should show the magnitude in db on the Y-axis (displayed by a red bar) for every frequency (in my case 512 values) calculated by a FFT on the X-Axis.
The output should look like this:
I fill a buffer with 1024 samples extracting only the left channel for the beginning. Then I do all this FFT stuff.
Here is my code so far:
Setting up some variables
- (void)setupVars
{
maxSamples = 1024;
log2n = log2f(maxSamples);
n = 1 << log2n;
stride = 1;
nOver2 = maxSamples/2;
A.realp = (float *) malloc(nOver2 * sizeof(float));
A.imagp = (float *) malloc(nOver2 * sizeof(float));
memset(A.imagp, 0, nOver2 * sizeof(float));
obtainedReal = (float *) malloc(n * sizeof(float));
originalReal = (float *) malloc(n * sizeof(float));
setupReal = vDSP_create_fftsetup(log2n, FFT_RADIX2);
}
Doing the FFT. FrequencyArray is just a data structure that holds 512 float values.
- (FrequencyArry)performFastFourierTransformForSampleData:(SInt16*)sampleData andSampleRate:(UInt16)sampleRate
{
NSLog(#"log2n %i n %i, nOver2 %i", log2n, n, nOver2);
// n = 1024
// log2n 10
// nOver2 = 512
for (int i = 0; i < n; i++) {
originalReal[i] = (float) sampleData[i];
}
vDSP_ctoz((COMPLEX *) originalReal, 2, &A, 1, nOver2);
vDSP_fft_zrip(setupReal, &A, stride, log2n, FFT_FORWARD);
float scale = (float) 1.0 / (2 * n);
vDSP_vsmul(A.realp, 1, &scale, A.realp, 1, nOver2);
vDSP_vsmul(A.imagp, 1, &scale, A.imagp, 1, nOver2);
vDSP_ztoc(&A, 1, (COMPLEX *) obtainedReal, 2, nOver2);
FrequencyArry frequencyArray;
for (int i = 0; i < nOver2; i++) {
frequencyArray.frequency[i] = log10f(obtainedReal[i]); // Magnitude in db???
}
return frequencyArray;
}
The output looks always kind of weird although it some how seems to move according to the music.
I'm happy that I came so far thanks to some very good posts here like this:
Using the apple FFT and accelerate Framework
But now I don't know what to do. What am I missing?
Firstly you're not applying a window function prior to the FFT - this will result in smearing of the spectrum due to spectral leakage.
Secondly, you're just using the real component of the FFT output bins to calculate dB magnitude - you need to use the complex magnitude:
magnitude_dB = 10 * log10(re * re + im * im);
I've been using this web page as a guideline for formant tracking of speech...
http://iitg.vlab.co.in/?sub=59&brch=164&sim=615&cnt=1
It all seems to be going pretty well, except for the last step, which is the converting of the cepstrum into a smoothed representation for simple peak picking for the formant tracking. The spectrograph looks good, and the cepstrograph (can I say that? :P) also looks good (from what I can tell), but the final stage the results (smoothed formant representation) are not what I expected.
I uploaded a sample of each stage as visual images to...
http://imgur.com/a/62duS
This sample is for the speech of the sound 'i' as in 'beed'. According to this site...
http://home.cc.umanitoba.ca/~robh/howto.html#formants
the first formant should come in around 500hz, and the second and third around 2200hz and 2800 hz respectively. The spetrograph shows something very similar, but on the last stage I am gettings results similar to...
F1 - 891
F2 - 1550
F3 - 2329
Any insight would be greatly appreciated. I've been going round in circles on this for some time. My code looks as follows...
// set up fft parameters
UInt32 log2n = 9;
UInt32 n = 512;
UInt32 window = n;
UInt32 halfN = n/2;
UInt32 stride = 1;
FFTSetup setupReal = [appDelegate getFftSetup];
int stepSize = (hpBuffer.sampleCount-window) / quantizeCount;
// calculate volume from raw samples, because it seems more reliable that fft
UInt32 volumeWindow = 128;
volumeBuffer = malloc(sizeof(float)*quantizeCount);
int windowPos = 0;
for (int i=0; i < quantizeCount; i++) {
windowPos += stepSize;
float total = 0.0f;
float max = 0.0f;
for (int p=windowPos; p < windowPos+volumeWindow; p++) {
total += sampleBuffer.buffer[p];
if (sampleBuffer.buffer[p] > max)
max = sampleBuffer.buffer[p];
}
volumeBuffer[i] = max;
}
// normalize volumebuffer
[FloatAudioBuffer normalizePositiveBuffer:volumeBuffer ofSize:quantizeCount];
// allocate memory for complex array
COMPLEX_SPLIT complexArray;
complexArray.realp = (float*)malloc(4096*sizeof(float));
complexArray.imagp = (float*)malloc(4096*sizeof(float));
// allocate some space for temporary hamming buffer
float *hamBuffer = malloc(n*sizeof(float));
// create spectrum and feature buffer
spectrumBuffer = malloc(sizeof(float)*halfN*quantizeCount);
formantBuffer = malloc(sizeof(float)*4096*quantizeCount);
cepstrumBuffer = malloc(sizeof(float)*halfN*quantizeCount);
lowCepstrumBuffer = malloc(sizeof(float)*featureCount*quantizeCount);
featureBuffer = malloc(sizeof(float)*featureCount*quantizeCount);
// create data point for each quantize segment
float TWOPI = 2.0f * M_PI;
for (int s=0; s < quantizeCount; s++) {
// copy buffer data into a seperate array and apply hamming window
int offset = (int)(s * stepSize);
for (int i=0; i < n; i++)
hamBuffer[i] = hpBuffer.buffer[offset+i] * ((1.0f-0.46f) - 0.46f*cos(TWOPI*i/((float)n-1.0f)));
// configure float array into acceptable input array format (interleaved)
vDSP_ctoz((COMPLEX*)hamBuffer, 2, &complexArray, 1, halfN);
// run FFT
vDSP_fft_zrip(setupReal, &complexArray, stride, log2n, FFT_FORWARD);
// Absolute square (equivalent to mag^2)
complexArray.imagp[0] = 0.0f;
vDSP_zvmags(&complexArray, 1, complexArray.realp, 1, halfN);
bzero(complexArray.imagp, (halfN) * sizeof(float));
// scale
float scale = 1.0f / (2.0f*(float)n);
vDSP_vsmul(complexArray.realp, 1, &scale, complexArray.realp, 1, halfN);
// get log of absolute values for passing to inverse FFT for cepstrum
for (int i=0; i < halfN; i++)
complexArray.realp[i] = logf(sqrtf(complexArray.realp[i]));
// save this into spectrum buffer
memcpy(&spectrumBuffer[s*halfN], complexArray.realp, halfN*sizeof(float));
// convert spectrum to interleaved ready for inverse fft
vDSP_ctoz((COMPLEX*)&spectrumBuffer[s*halfN], 2, &complexArray, 1, halfN/2);
// create cepstrum
vDSP_fft_zrip(setupReal, &complexArray, stride, log2n-1, FFT_INVERSE);
//convert interleaved to real and straight into cepstrum buffer
vDSP_ztoc(&complexArray, 1, (COMPLEX*)&cepstrumBuffer[s*halfN], 2, halfN/2);
// copy first part of cepstrum into low cepstrum buffer
memcpy(&lowCepstrumBuffer[s*featureCount], &cepstrumBuffer[s*halfN], featureCount*sizeof(float));
// make 8000 point array based on the first 15 values
float *tempArray = malloc(8192*sizeof(float));
for (int i=0; i < 8192; i++) {
if (i < 15)
tempArray[i] = cepstrumBuffer[s*halfN+i];
else
tempArray[i] = 0.0f;
}
vDSP_ctoz((COMPLEX*)tempArray, 2, &complexArray, 1, 4096);
float newLog2n = log2f(8192.0f);
complexArray.imagp[0] = 0.0f;
vDSP_fft_zrip(setupReal, &complexArray, stride, newLog2n, FFT_FORWARD);
vDSP_zvmags(&complexArray, 1, complexArray.realp, 1, 4096);
bzero(complexArray.imagp, (4096) * sizeof(float));
// scale
scale = 1.0f / (2.0f*(float)8192);
vDSP_vsmul(complexArray.realp, 1, &scale, complexArray.realp, 1, 4096);
// get magnitude
for (int i=0; i < 4096; i++)
complexArray.realp[i] = sqrtf(complexArray.realp[i]);
// write to formant buffer
memcpy(&formantBuffer[s*4096], complexArray.realp, 4096*sizeof(float));
// complex array now contains formant spectrum
// it's large, so get features here!
// try simple peak picking algorithm for first 3 formants
int formantIndex = 0;
float *peaks = malloc(6*sizeof(float));
for (int i=0; i < 6; i++)
peaks[i] = 0.0f;
for (int i=1; i < 4096-1 && formantIndex < 6; i++) {
if (complexArray.realp[i-1] < complexArray.realp[i] &&
complexArray.realp[i+1] < complexArray.realp[i])
peaks[formantIndex++] = i;
}