/// <summary>
/// Convolves the specified finite signal with an infinite signal.
/// </summary>
/// <param name="s1">The finite signal.</param>
/// <param name="s2">The infinite signal.</param>
/// <returns></returns>
/// <exception cref="SamplerateMismatchException"></exception>
public static ISignal Convolve(this IFiniteSignal s1, ISignal s2)
{
if (s1.SampleRate != s2.SampleRate)
{
throw new SamplerateMismatchException();
}
return(new InfiniteSignal(
(start, length) =>
{
var l = s1.Length + Math.Max(s1.Length, length) - 1;
var n = Fft.NextPowerOfTwo(l);
var spectrum1 = Fft.RealFft(s1.Signal, n);
var signal2A = s2.GetWindowedSignal(start - s1.Length - s1.Start, s1.Length);
var spectrum2A = Fft.RealFft(signal2A, n);
var signal2B = s2.GetWindowedSignal(start - s1.Start, length);
var spectrum2B = Fft.RealFft(signal2B, n);
var spectrumA = spectrum1.Multiply(spectrum2A);
var spectrumB = spectrum1.Multiply(spectrum2B);
var signalA = Fft.RealIfft(spectrumA).Skip(s1.Length).Take(Math.Min(length, s1.Length - 1));
var signalB = Fft.RealIfft(spectrumB).Take(length);
var signal = signalA.AddFull(signalB);
return signal;
},
s1.SampleRate)
{
DisplayName = "convolution result"
});
}
/// <summary>
/// Pitch estimation: from spectral peaks
/// </summary>
/// <param name="signal"></param>
/// <param name="startPos"></param>
/// <param name="endPos"></param>
/// <returns></returns>
public static float FromSpectralPeaks(DiscreteSignal signal,
int startPos = 0,
int endPos = -1,
float low = 80,
float high = 400,
int fftSize = 0)
{
if (endPos == -1)
{
endPos = signal.Length;
}
if (startPos != 0 || endPos != signal.Length)
{
signal = signal[startPos, endPos];
}
signal.ApplyWindow(WindowTypes.Hann);
var size = fftSize > 0 ? fftSize : MathUtils.NextPowerOfTwo(signal.Length);
var fft = new Fft(size);
var spectrum = fft.PowerSpectrum(signal, false).Samples;
return(FromSpectralPeaks(spectrum, signal.SamplingRate, low, high));
}
/*
* Computes the unscaled DCT type III on the specified array in place.
* The array length must be a power of 2 or zero.
* For the formula, see https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-III .
*/
public static void InverseTransform(double[] vector)
{
if (vector == null)
{
throw new NullReferenceException();
}
int len = vector.Length;
if (len > 0)
{
vector[0] /= 2;
}
Complex[] temp = new Complex[len];
for (int i = 0; i < len; i++)
{
temp[i] = vector[i] * Complex.Exp(new Complex(0, -i * Math.PI / (len * 2)));
}
Fft.Transform(temp, false);
int halfLen = len / 2;
for (int i = 0; i < halfLen; i++)
{
vector[i * 2 + 0] = temp[i].Real;
vector[i * 2 + 1] = temp[len - 1 - i].Real;
}
if (len % 2 == 1)
{
vector[len - 1] = temp[halfLen].Real;
}
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="noise"></param>
/// <param name="fftSize"></param>
/// <param name="hopSize"></param>
public SpectralSubtractor(DiscreteSignal noise, int fftSize = 1024, int hopSize = 128)
{
Guard.AgainstInvalidRange(hopSize, fftSize, "Hop size", "FFT size");
_fftSize = fftSize;
_hopSize = hopSize;
_overlapSize = _fftSize - _hopSize;
_fft = new Fft(_fftSize);
_window = Window.OfType(WindowTypes.Hann, _fftSize);
_gain = 2 / (_fftSize * _window.Select(w => w * w).Sum() / _hopSize);
_noiseAcc = new float[_fftSize / 2 + 1];
_noiseEstimate = new float[_fftSize / 2 + 1];
_dl = new float[_fftSize];
_re = new float[_fftSize];
_im = new float[_fftSize];
_filteredRe = new float[_fftSize];
_filteredIm = new float[_fftSize];
_zeroblock = new float[_fftSize];
_lastSaved = new float[_overlapSize];
EstimateNoise(noise);
Reset();
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="kernel"></param>
/// <param name="fftSize"></param>
public OlaBlockConvolver(IEnumerable <float> kernel, int fftSize)
{
_fftSize = MathUtils.NextPowerOfTwo(fftSize);
if (kernel.Count() > _fftSize)
{
throw new ArgumentException("Kernel length must not exceed the size of FFT!");
}
_fft = new Fft(_fftSize);
_kernel = kernel.ToArray();
_kernelSpectrumRe = _kernel.PadZeros(_fftSize);
_kernelSpectrumIm = new float[_fftSize];
_convRe = new float[_fftSize];
_convIm = new float[_fftSize];
_blockRe = new float[_fftSize];
_blockIm = new float[_fftSize];
_lastSaved = new float[_kernel.Length - 1];
_zeroblock = new float[_fftSize];
_fft.Direct(_kernelSpectrumRe, _kernelSpectrumIm);
Reset();
}
private void openToolStripMenuItem_Click(object sender, EventArgs e)
{
var ofd = new OpenFileDialog();
if (ofd.ShowDialog() != DialogResult.OK)
{
return;
}
using (var stream = new FileStream(ofd.FileName, FileMode.Open))
{
var waveFile = new WaveFile(stream);
_signal = waveFile[Channels.Left];
}
_fft = new Fft(512);
var lpcExtractor = new LpcExtractor(16, FrameSize, HopSize);
_lpcVectors = lpcExtractor.ParallelComputeFrom(_signal);
FillFeaturesList(_lpcVectors, lpcExtractor.FeatureDescriptions);
lpcListView.Items[0].Selected = true;
spectrumPanel.Line = ComputeSpectrum(0);
spectrumPanel.Markline = EstimateSpectrum(0);
spectrumPanel.ToDecibel();
lpcPanel.Line = _lpcVectors[0].Features.Skip(1).ToArray();
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="samplingRate"></param>
/// <param name="shift"></param>
/// <param name="fftSize"></param>
/// <param name="hopSize"></param>
public PitchShiftVocoderEffect(int samplingRate, double shift, int fftSize = 1024, int hopSize = 64)
{
_shift = (float)shift;
_fftSize = fftSize;
_hopSize = hopSize;
_overlapSize = _fftSize - _hopSize;
Guard.AgainstInvalidRange(_hopSize, _fftSize, "Hop size", "FFT size");
_fft = new Fft(_fftSize);
_window = Window.OfType(WindowTypes.Hann, _fftSize);
_gain = (float)(2 * Math.PI / (_fftSize * _window.Select(w => w * w).Sum() / _hopSize));
_freqResolution = samplingRate / _fftSize;
_dl = new float[_fftSize];
_re = new float[_fftSize];
_im = new float[_fftSize];
_filteredRe = new float[_fftSize];
_filteredIm = new float[_fftSize];
_zeroblock = new float[_fftSize];
_lastSaved = new float[_overlapSize];
_mag = new float[_fftSize / 2 + 1];
_phase = new float[_fftSize / 2 + 1];
_prevPhase = new float[_fftSize / 2 + 1];
_phaseTotal = new float[_fftSize / 2 + 1];
}
public void GetAmpSpectrumAndMax_ShortVec_sameResult()
{
List <double> Vector = CreateVector(1, 13, -10, 10);
FftResult fft = new FftResult();
FftResult result = Fft.GetAmpSpectrumAndMax(1, Vector);
for (int i = 0; i < result.Values.Count; i++)
{
result.Values[i] *= 1e13;
result.Values[i] = Math.Round(result.Values[i], 2, MidpointRounding.AwayFromZero);
}
List <(double, double)> valFreqTuple = new List <(double, double)>()
{
(0d, 0d), (3.25, 0.05), (1.49, 0.1), (0.16, 0.15), (1.36, 0.2), (1.27, 0.25), (0.03, 0.3), (1.46, 0.35), (1.92, 0.4), (1.31, 0.45), (0.54, 0.5)
};
valFreqTuple.ForEach(x => { fft.Values.Add(x.Item1); fft.Frequencies.Add(x.Item2); });
fft.MaxIndex = 1;
Assert.Equal(result.Frequencies, fft.Frequencies);
Assert.Equal(result.Values, fft.Values);
Assert.Equal(result.MaxIndex, fft.MaxIndex);
/*Hodnoty nejsou přesně shodné s matlabem(Matlab zaokrouhluje jinak než Math.Net)
* Math.Net počítá s větší přesností (e-21, matlab to zaokrouhlí na nulu)*/
}
public void GetAmpSpectrumAndMax_ShortVec1_sameResult()
{
List <double> vecX = new List <double>()
{
319.5, 319.570564104974, 319.570564104974, 319.387187167831
};
List <double> vecY = new List <double>()
{
239.5, 239.690534471355, 239.690534471355, 239.447377199610
};
FftResult resultX = Fft.GetAmpSpectrumAndMax(29.966, vecX);
FftResult resultY = Fft.GetAmpSpectrumAndMax(29.966, vecY);
resultY.Values = resultY.Values.Select(x => Math.Round(x, 7)).ToList();
resultX.Values = resultX.Values.Select(x => Math.Round(x, 7)).ToList();
List <double> matlabValuesX = new List <double>()
{
0, 0.1964851
};
List <double> matlabValuesY = new List <double>()
{
0, 0.3089156
};
Assert.Equal(matlabValuesX, resultX.Values);
Assert.Equal(matlabValuesY, resultY.Values);
}
public void GetAmpSpectrumAndMax_EmptyInputList_returnsNull()
{
List <double> vector = new List <double>();
var result = Fft.GetAmpSpectrumAndMax(100, vector);
Assert.Null(result);
}
/// <summary>
/// Fast convolution via FFT for arrays of samples (maximally in-place).
/// This version is best suited for block processing when memory needs to be reused.
/// Input arrays must have size equal to the size of FFT.
/// </summary>
/// <param name="real1">Real parts of the 1st signal (zero-padded)</param>
/// <param name="imag1">Imaginary parts of the 1st signal (zero-padded)</param>
/// <param name="real2">Real parts of the 2nd signal (zero-padded)</param>
/// <param name="imag2">Imaginary parts of the 2nd signal (zero-padded)</param>
/// <param name="res">Real parts of resulting convolution (zero-padded if center == 0)</param>
/// <param name="center">
/// Position of central sample for the case of 2*M-1 convolution
/// (if it is set then resulting array has length of M)
/// </param>
public static void Convolve(float[] real1, float[] imag1, float[] real2, float[] imag2, float[] res, int center = 0)
{
var fftSize = real1.Length;
var fft = new Fft(fftSize);
// 1) do FFT of both signals
fft.Direct(real1, imag1);
fft.Direct(real2, imag2);
// 2) do complex multiplication of spectra and normalize
for (var i = 0; i < fftSize; i++)
{
var re = real1[i] * real2[i] - imag1[i] * imag2[i];
var im = real1[i] * imag2[i] + imag1[i] * real2[i];
real1[i] = re / fftSize;
imag1[i] = im / fftSize;
}
// 3) do inverse FFT of resulting spectrum
fft.Inverse(real1, imag1);
// 4) return output array
if (center > 0)
{
real1.FastCopyTo(res, center, center - 1);
}
else
{
real1.FastCopyTo(res, fftSize);
}
}
public double[] Spectrum(double[] input, bool scale)
{
int length = input.Length;
var fft = new Fft(length);
var re = new float[length];
var im = new float[length];
for (int i = 0; i < length; i++)
{
re[i] = (float)input[i];
im[i] = 0f;
}
fft.Direct(re, im);
var spectrum = Helper.ComputeSpectrum(length, re, im);
fft.Inverse(re, im);
for (int i = 0; i < length; i++)
{
input[i] = re[i];
}
return(spectrum);
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="stretch"></param>
/// <param name="hopAnalysis"></param>
/// <param name="fftSize"></param>
/// <param name="phaseLocking"></param>
public PhaseLockingVocoder(double stretch, int hopAnalysis, int fftSize = 0, bool phaseLocking = true)
{
_stretch = stretch;
_hopAnalysis = hopAnalysis;
_hopSynthesis = (int)(hopAnalysis * stretch);
_fftSize = (fftSize > 0) ? fftSize : 8 * Math.Max(_hopAnalysis, _hopSynthesis);
_phaseLocking = phaseLocking;
_fft = new Fft(_fftSize);
_window = Window.OfType(WindowTypes.Hann, _fftSize);
_gain = 2 / (_fftSize * _window.Select(w => w * w).Sum() / _hopSynthesis);
_omega = Enumerable.Range(0, _fftSize / 2 + 1)
.Select(f => 2 * Math.PI * f / _fftSize)
.ToArray();
_mag = new double[_fftSize / 2 + 1];
_phase = new double[_fftSize / 2 + 1];
_prevPhase = new double[_fftSize / 2 + 1];
_phaseTotal = new double[_fftSize / 2 + 1];
_delta = new double[_fftSize / 2 + 1];
_peaks = new int[_fftSize / 4];
_re = new float[_fftSize];
_im = new float[_fftSize];
_zeroblock = new float[_fftSize];
}
public FftArrayVsSpan()
{
_fft = new RealFft(FrameSize);
_fft64 = new RealFft64(FrameSize);
_complexFft = new Fft(FrameSize);
_samples = new WhiteNoiseBuilder().OfLength(N).Build().Samples;
_samples64 = _samples.ToDoubles();
}
public void TestFftShift()
{
float[] array = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 };
Fft.Shift(array);
Assert.That(array, Is.EqualTo(new float[] { 8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7 }));
}
private void UpdateSpectrumAndCepstrum()
{
var fftSize = int.Parse(fftSizeTextBox.Text);
var cepstrumSize = int.Parse(cepstrumSizeTextBox.Text);
_hopSize = int.Parse(hopSizeTextBox.Text);
if (fftSize != _fftSize)
{
_fftSize = fftSize;
_fft = new Fft(fftSize);
_cepstralTransform = new CepstralTransform(cepstrumSize, _fftSize);
}
if (cepstrumSize != _cepstrumSize)
{
_cepstrumSize = cepstrumSize;
_cepstralTransform = new CepstralTransform(_cepstrumSize, _fftSize);
}
var pos = _hopSize * _specNo;
var block = _signal[pos, pos + _fftSize];
//block.ApplyWindow(WindowTypes.Hamming);
var cepstrum = _cepstralTransform.Direct(block);
var pitch = Pitch.FromCepstrum(block);
// ************************************************************************
// just visualize spectrum estimated from cepstral coefficients:
// ************************************************************************
var real = new float[_fftSize];
var imag = new float[_fftSize];
for (var i = 0; i < 32; i++)
{
real[i] = cepstrum[i];
}
_fft.Direct(real, imag);
var spectrum = _fft.PowerSpectrum(block, normalize: false).Samples;
var avg = spectrum.Average(s => LevelScale.ToDecibel(s));
var spectrumEstimate = real.Take(_fftSize / 2 + 1)
.Select(s => (float)LevelScale.FromDecibel(s * 40 / _fftSize - avg))
.ToArray();
spectrumPanel.Line = spectrum;
spectrumPanel.Markline = spectrumEstimate;
spectrumPanel.ToDecibel();
cepstrumPanel.Line = cepstrum.Samples;
cepstrumPanel.Mark = (int)(_signal.SamplingRate / pitch);
}
/// <summary>
/// Main constructor
/// </summary>
/// <param name="samplingRate"></param>
/// <param name="featureCount"></param>
/// <param name="frameDuration"></param>
/// <param name="hopDuration"></param>
/// <param name="filterbankSize"></param>
/// <param name="lowFreq"></param>
/// <param name="highFreq"></param>
/// <param name="fftSize"></param>
/// <param name="filterbank"></param>
/// <param name="lifterSize"></param>
/// <param name="preEmphasis"></param>
/// <param name="window"></param>
public MfccExtractor(int samplingRate,
int featureCount,
double frameDuration = 0.0256 /*sec*/,
double hopDuration = 0.010 /*sec*/,
int filterbankSize = 20,
double lowFreq = 0,
double highFreq = 0,
int fftSize = 0,
float[][] filterbank = null,
int lifterSize = 22,
double preEmphasis = 0.0,
WindowTypes window = WindowTypes.Hamming)
: base(samplingRate, frameDuration, hopDuration)
{
FeatureCount = featureCount;
if (filterbank == null)
{
_fftSize = fftSize > FrameSize ? fftSize : MathUtils.NextPowerOfTwo(FrameSize);
_filterbankSize = filterbankSize;
_lowFreq = lowFreq;
_highFreq = highFreq;
FilterBank = FilterBanks.Triangular(_fftSize, SamplingRate,
FilterBanks.MelBands(_filterbankSize, _fftSize, SamplingRate, _lowFreq, _highFreq));
}
else
{
FilterBank = filterbank;
_filterbankSize = filterbank.Length;
_fftSize = 2 * (filterbank[0].Length - 1);
}
_fft = new Fft(_fftSize);
_dct = new Dct2(_filterbankSize, FeatureCount);
_window = window;
if (_window != WindowTypes.Rectangular)
{
_windowSamples = Window.OfType(_window, FrameSize);
}
_lifterSize = lifterSize;
_lifterCoeffs = _lifterSize > 0 ? Window.Liftering(FeatureCount, _lifterSize) : null;
_preEmphasis = (float)preEmphasis;
// reserve memory for reusable blocks
_spectrum = new float[_fftSize / 2 + 1];
_logMelSpectrum = new float[_filterbankSize];
_block = new float[_fftSize];
_zeroblock = new float[_fftSize];
}
private static void Main(string[] args)
{
/*var files = new List<string>();
* var data = new List<(List<double>, string)>();
* var percent = 0.0;
* Console.WriteLine($"Percent: {percent * 100}%");
* foreach (var genre in Genres)
* {
* for (var i = 0; i < 5; i++)
* {
* var file = $@"C:\Users\Ale\Licenta-master\Licenta-master\PopulateDatabase\music\genres\{genre}\{genre}.0000{i}.wav";
* var sound = SoundReader.ReadFromFile(file);
* var fftResult = Fft.CalculateFft(sound, keepAll: true);
* foreach (var res in fftResult.Result)
* data.Add((res.Points.Select(x => (double)x).ToList(), genre));
* percent += 1.0 / 50;
* Console.Clear();
*
* Console.WriteLine($"Percent: {percent * 100}%");
* }
* }*/
var ann = new RNA(null, AnnMode.Training, ActivationFunctions.Tahn);
var net = Utils.ReadFromBinaryFile <List <List <Neuron> > >("net");
//var net = ann.NetInit(10, 9);
//ann.Training(ref net, 10, 0.001, 1000);
var sound2 = SoundReader.ReadFromFile(
@"C:\Users\Ale\Licenta-master\Licenta-master\PopulateDatabase\music\genres\disco\disco.00001.wav");
var result2 = Fft.CalculateFft(sound2, keepAll: true);
//var realOutputs = ann.TestData.Select(x => x.Item2).ToList();
ann.TestData = result2.Result.Select(x => (x.HighScores, "disco")).ToList();
ann.TestData.Shuffle();
var computedOutputs = ann.Evaluate(ref net, 10);
var genres = ann.GetGeneres();
foreach (var result in computedOutputs)
{
genres[ann.GetValueFromLabelVector(result)] += 1;
}
var s = genres.ToDictionary(x => x.Key, x => x.Value * 100.0 / computedOutputs.Count);
//Console.Write(ann.ComputePerformance(computedOutputs, realOutputs));
Utils.WriteToBinaryFile("net", net);
Console.ReadKey();
}
/// <summary>
/// Prepare all necessary arrays for calculations
/// </summary>
/// <param name="fftSize"></param>
private void PrepareMemory(int fftSize)
{
_fftSize = fftSize;
_fft = new Fft(_fftSize);
_real1 = new float[_fftSize];
_imag1 = new float[_fftSize];
_real2 = new float[_fftSize];
_imag2 = new float[_fftSize];
_zeroblock = new float[_fftSize];
}
private static void TestConvolution(int size)
{
Complex[] input0vector = RandomComplexes(size);
Complex[] input1vector = RandomComplexes(size);
Complex[] refoutvector = new Complex[size];
NaiveConvolve(input0vector, input1vector, refoutvector);
Complex[] actualoutvector = new Complex[size];
Fft.Convolve(input0vector, input1vector, actualoutvector);
Console.WriteLine("convsize={0,4} logerr={1,5:F1}", size, Log10RmsErr(refoutvector, actualoutvector));
}
/// <summary>
/// Constuctor
/// </summary>
/// <param name="hopSize"></param>
/// <param name="fftSize"></param>
public WhisperEffect(int hopSize, int fftSize = 0)
{
_hopSize = hopSize;
_fftSize = (fftSize > 0) ? fftSize : 8 * hopSize;
_fft = new Fft(_fftSize);
_window = Window.OfType(WindowTypes.Hann, _fftSize);
_re = new float[_fftSize];
_im = new float[_fftSize];
_zeroblock = new float[_fftSize];
}
public void TestInverseFftNormalized()
{
float[] re = { 1, 5, 3, 7, 2, 3, 0, 7 };
float[] im = new float[re.Length];
var fft = new Fft(8);
fft.Direct(re, im);
fft.InverseNorm(re, im);
Assert.That(re, Is.EqualTo(new[] { 1, 5, 3, 7, 2, 3, 0, 7 }).Within(1e-5));
}
public void TestComplexFft()
{
float[] re = { 0, 1, 2, 3, 4, 5, 6, 7 };
float[] im = new float[8];
var fft = new Fft(8);
fft.Direct(re, im);
Assert.That(re, Is.EqualTo(new float[] { 28, -4, -4, -4, -4, -4, -4, -4 }).Within(1e-5));
Assert.That(im, Is.EqualTo(new float[] { 0, 9.65685f, 4, 1.65685f, 0, -1.65685f, -4, -9.65685f }).Within(1e-5));
}
/// <summary>
/// Compute the sequence of feature vectors from some fragment of a signal
/// </summary>
/// <param name="signal">Signal</param>
/// <param name="startSample">The number (position) of the first sample for processing</param>
/// <param name="endSample">The number (position) of last sample for processing</param>
/// <returns>Sequence of feature vectors</returns>
public override List <FeatureVector> ComputeFrom(DiscreteSignal signal, int startSample, int endSample)
{
var frameSize = (int)(signal.SamplingRate * FrameSize);
var hopSize = (int)(signal.SamplingRate * HopSize);
var fftSize = _fftSize >= frameSize ? _fftSize : MathUtils.NextPowerOfTwo(frameSize);
var resolution = (float)signal.SamplingRate / fftSize;
var frequencies = Enumerable.Range(0, fftSize + 1)
.Select(f => f * resolution)
.ToArray();
var featureVectors = new List <FeatureVector>();
var featureCount = FeatureCount;
var fft = new Fft(fftSize);
// reserve memory for reusable blocks
var spectrum = new float[fftSize / 2 + 1]; // buffer for magnitude spectrum
var block = new float[fftSize]; // buffer for currently processed block
var zeroblock = new float[fftSize]; // just a buffer of zeros for quick memset
var i = startSample;
while (i + frameSize < endSample)
{
// prepare all blocks in memory for the current step:
zeroblock.FastCopyTo(block, fftSize);
signal.Samples.FastCopyTo(block, frameSize, i);
fft.MagnitudeSpectrum(block, spectrum);
var featureVector = new float[featureCount];
for (var j = 0; j < featureCount; j++)
{
featureVector[j] = _extractors[j](spectrum, frequencies);
}
featureVectors.Add(new FeatureVector
{
Features = featureVector,
TimePosition = (double)i / signal.SamplingRate
});
i += hopSize;
}
return(featureVectors);
}
public void TestInverseFft()
{
float[] re = { 1, 5, 3, 7, 2, 3, 0, 7 };
float[] im = new float[re.Length];
var fft = new Fft(8);
fft.Direct(re, im);
fft.Inverse(re, im);
Assert.That(re, Is.EqualTo(new[] { 8, 40, 24, 56, 16, 24, 0, 56 }).Within(1e-5));
// re[i] * 8, i = 0..7
}
/// <summary>
/// Constuctor
/// </summary>
/// <param name="hopSize"></param>
/// <param name="fftSize"></param>
public RobotEffect(int hopSize, int fftSize = 0)
{
_hopSize = hopSize;
_fftSize = (fftSize > 0) ? fftSize : 8 * hopSize;
_fft = new Fft(_fftSize);
_window = Window.OfType(WindowTypes.Hann, _fftSize);
_gain = (float)(2 * Math.PI / (_fftSize * _window.Select(w => w * w).Sum() / _hopSize));
_re = new float[_fftSize];
_im = new float[_fftSize];
_zeroblock = new float[_fftSize];
}
public void ComputeFftDuringSignalForTwoSignals_BasicTests_GetCorectResult()
{
double fs = 30;
List <double> vector1 = Enumerable.Range(0, 256).Select(x => (double)x).ToList();
List <double> vector2 = Enumerable.Range(5, 256).Select(x => (double)x).ToList();
List <double> expected = new List <double>();
List <double> vys = Fft.ComputeFftDuringSignalForTwoSignals(fs, vector1, vector2, 256, 1, false);
for (int i = 0; i < 1040; i++)
{
expected.Add(7);
}
Assert.Equal(expected, vys);
}
// Update spectra after filter
private void UpdateSpectraAfterFilter()
{
var fftSize = int.Parse(fftSizeTextBox.Text);
var cepstrumSize = int.Parse(cepstrumSizeTextBox.Text);
_hopSize = int.Parse(hopSizeTextBox.Text);
if (fftSize != _fftSize)
{
_fftSize = fftSize;
_fft = new Fft(fftSize);
_cepstralTransform = new CepstralTransform(cepstrumSize, _fftSize);
}
if (cepstrumSize != _cepstrumSize)
{
_cepstrumSize = cepstrumSize;
_cepstralTransform = new CepstralTransform(_cepstrumSize, _fftSize);
}
var pos = _hopSize * _specNo;
var block = _signal[pos, pos + _fftSize];
block.ApplyWindow(WindowTypes.Hamming);
var cepstrum = _cepstralTransform.Direct(block);
var real = new float[_fftSize];
var imag = new float[_fftSize];
for (var i = 0; i < 32; i++)
{
real[i] = cepstrum[i];
}
_fft.Direct(real, imag);
var spectrum = _fft.PowerSpectrum(block, normalize: false).Samples;
var avg = spectrum.Average(s => LevelScale.ToDecibel(s));
var spectrumEstimate = real.Take(_fftSize / 2 + 1)
.Select(s => (float)LevelScale.FromDecibel(s * 40 / _fftSize - avg))
.ToArray();
spectrumPanelAfterFilter.Line = spectrum;
spectrumPanelAfterFilter.Markline = spectrumEstimate;
spectrumPanelAfterFilter.ToDecibel();
spectrumPanelAfterFilter.max_freq_value = spectrum.Length * _signal.SamplingRate / fftSize;
}
public void GetAmpSpectrumAndMax_MultipleMaxIndexes_SameMaxIndexes()
{
List <double> freq_matlab = new List <double>();
List <double> freq_math_dotnet = new List <double>();
double f;
for (int i = 1; i <= 45; i++)
{
f = i * 20;
freq_matlab.Add(f);
List <double> Vector = CreateVector(100, i, -10, 10);
freq_math_dotnet.Add(Fft.GetAmpSpectrumAndMax(i, Vector).MaxIndex);
}
Assert.Equal(freq_matlab, freq_math_dotnet);
}
public void Initialize(double[] data)
{
int length = Size = data.Length;
fft = new Fft(length);
re = new float[length];
im = new float[length];
for (int i = 0; i < length; i++)
{
re[i] = (float)data[i];
im[i] = 0f;
}
}
void Awake()
{
m_transFourrier = new Fft();
}
