/// <summary>
/// Create log-spectrogram (spaced according to manager's parameters)
/// </summary>
/// <param name = "proxy">Proxy used in generating the spectrogram</param>
/// <param name = "filename">Filename to be processed</param>
/// <param name = "milliseconds">Milliseconds to be analyzed</param>
/// <param name = "startmilliseconds">Starting point</param>
/// <returns>Logarithmically spaced bins within the power spectrum</returns>
public float[][] CreateLogSpectrogram(IWaveformPlayer proxy, string filename, int milliseconds, int startmilliseconds)
{
//read 5512 Hz, Mono, PCM, with a specific proxy
float[] samples = BassProxy.ReadMonoFromFile(filename, SampleRate, milliseconds, startmilliseconds);
//NormalizeInPlace(samples);
int overlap = Overlap;
int fftWindowsSize = WdftSize;
double[] windowArray = FFTWindow.GetWindowFunction(FFTWindowType.HANNING, fftWindowsSize);
int width = (samples.Length - fftWindowsSize) / overlap; /*width of the image*/
float[][] frames = new float[width][];
float[] complexSignal = new float[2 * fftWindowsSize]; /*even - Re, odd - Img*/
for (int i = 0; i < width; i++)
{
//take 371 ms each 11.6 ms (2048 samples each 64 samples)
for (int j = 0; j < fftWindowsSize /*2048*/; j++)
{
// Weight by Hann Window
complexSignal[2 * j] = (float)(windowArray[j] * samples[i * overlap + j]);
//complexSignal[2*j] = (float) (_windowArray[j]*samples[i*overlap + j]); /*Weight by Hann Window*/
complexSignal[2 * j + 1] = 0;
}
//FFT transform for gathering the spectrum
Fourier.FFT(complexSignal, fftWindowsSize, FourierDirection.Forward);
frames[i] = ExtractLogBins(complexSignal);
}
return(frames);
}
private static void SimpleFFT()
{
ComplexF[] values = new ComplexF[1024];
ComplexF[] expectedValues = new ComplexF[1024];
Random rnd = new Random();
for (int i = 0; i < 1024; i++)
{
values[i] = new ComplexF(20, 0);
}
Array.Copy(values, expectedValues, expectedValues.Length);
Fourier.FFT(values, FourierDirection.Forward);
for (int i = 0; i < values.Length; i++)
{
Console.WriteLine(values[i]);
}
Fourier.FFT(values, FourierDirection.Backward);
for (int i = 0; i < values.Length; i++)
{
Console.WriteLine("{0} {1} {2}",
values[i] / values.Length
, expectedValues[i],
values[i] / expectedValues[i]);
}
}
/// <summary>
/// Compute a 1D fast Fourier transform of a dataset of complex numbers.
/// </summary>
/// <param name="data"></param>
/// <param name="direction"></param>
public static void FFT(ComplexF[] data, FourierDirection direction)
{
if (data == null)
{
throw new ArgumentNullException("data");
}
Fourier.FFT(data, data.Length, direction);
}
public override void FFT(bool forward)
{
data.CopyTo(copy, 0);
Fourier.FFT(copy, copy.Length, forward ?
FourierDirection.Forward :
FourierDirection.Backward);
}
public static FilterProfile Profile(ISoundObj impulse, SmoothingType type, double resolution)
{
uint nSR = impulse.SampleRate;
uint nSR2 = nSR / 2;
ushort nChannels = impulse.NumChannels;
for (ushort c = 0; c < nChannels; c++)
{
// Read channel into a buffer
SingleChannel channel = impulse.Channel(c);
SoundBuffer buff = new SoundBuffer(channel);
buff.ReadAll();
// And then double in length to prevent wraparound
buff.PadTo(buff.Count * 2);
// Pad to next higher power of two
buff.PadToPowerOfTwo();
// Read out into array of complex
Complex[][] data = buff.ToComplexArray();
Complex[] cdata = data[0];
// Then we're done with the buffer for this channel
buff = null;
GC.Collect();
// FFT in place
Fourier.FFT(cdata.Length, cdata);
int n = cdata.Length / 2;
// Now we have an array of complex, from 0Hz to Nyquist and back again.
// We really only care about the first half of the cdata buffer, but
// treat it as circular anyway (i.e. wrap around for negative values).
//
// We're only working with magnitudes from here on,
// so we can save some space by computing mags right away and storing them in the
// real part of the complex array; then we can use the imaginary portion for the
// smoothed data.
for (int j = 0; j < cdata.Length; j++)
{
cdata[j].Re = cdata[j].Magnitude;
cdata[j].Im = 0;
}
// Take a rectangular window of width (resolution)*(octave or ERB band)
// Add up all magnitudes falling within this window
//
// Move the window forward by one thingummajig
//double wMid = 0; // center of the window
//double wLen = 0;
}
return(new FilterProfile()); // temp
}
static private void Create_spectr()
{
lock (lockerDF)
{
data_furePic.Clear();
data_own_func.Clear();
float step = (float)1 / size / stept;
for (int i = 0; i < data_fure.Count; i++)
{
Complex[] buf = new Complex[size];
for (int j = 0; j < size; j++)
{
buf[j] = data_fure[i][j];
}
buf = Fourier.FFT(buf);
for (int j = 0; j < size; j++)
{
data_fure[i][j] = buf[j];
}
List <PointF> buf_vector = new List <PointF>();
for (int j = 0; j < size; j++)
{
PointF point = new PointF {
X = j * step, Y = (float)data_fure[i][j].Abs
};
buf_vector.Add(point);
}
data_furePic.Add(buf_vector);
}
for (int i = 0; i < size; i++) //-2 ибо не считаю спектры с концов, там всякий шум копится
{
List <PointF> buf_list = new List <PointF>();
for (int j = 0; j < data_furePic.Count; j++)
{
PointF buf = new PointF {
X = (float)xx[j], Y = (float)data_fure[j][i].Re
};
buf_list.Add(buf);
}
data_own_func.Add(buf_list);
}
isSpectrDone = true;
}
return;
}
public static double[] padded_FFT(Complex[] complexSignal)
{
int N = complexSignal.Length;
Fourier.FFT(complexSignal, N, FourierDirection.Forward);
// get the result
double[] fft_real = new double[N];
for (int j = 0; j < N; j++)
{
fft_real[j] = complexSignal[j].Re;
}
return(fft_real);
}
public override double[] Spectrum(double[] input, bool scale)
{
var data = ToComplex(input);
Fourier.FFT(data, data.Length, FourierDirection.Forward);
var spectrum = ComputeSpectrum(data);
Fourier.FFT(data, data.Length, FourierDirection.Backward);
ToDouble(data, input);
return(spectrum);
}
public static double[] padded_IFFT(double[] signal)
{
int N = signal.Length;
Complex[] complexSignal = FFTUtils.DoubleToComplex(signal);
Fourier.FFT(complexSignal, N, FourierDirection.Backward);
// get the result
double[] fft_real = new double[N];
for (int j = 0; j < N; j++)
{
fft_real[j] = complexSignal[j].Re;
}
return(fft_real);
}
/// <summary>
/// Applies a High Pass Filter to a Signal
/// </summary>
/// <param name="s">The Signal to filter</param>
/// <returns>The Filtered Signal</returns>
public static Signal High(Signal s)
{
Signal fft = Fourier.FFT(s);
int indexPass = (7 * 2) - 1;
Signal result = new Signal(s.Length);
for (int i = 0; i < result.Length; i++)
{
result[i] = 0;
}
for (int i = 15; i < result.Length - indexPass; i += 2)
{
result[i] = fft[i];
}
return(Fourier.InverseFFT(result));
}
public void AppendSample(byte[] data, uint time)
{
Complex[] fft = new Complex[N];
for (int i = 0; i < N; i++)
{
int byteOffset = 2 * i;
double val = hammingWindowCoeffs[i] * (short)(data[byteOffset] | data[byteOffset + 1] << 8);
fft[i] = new Complex(val, 0);
}
Fourier.FFT(N, fft);
var magnitudes = new double[6];
var frequencies = new uint[6];
for (uint freq = MinFrequency; freq < MaxFrequency - 1; freq++)
{
// Get the magnitude:
double mag = fft[freq].Magnitude;
// Find out which range we are in:
int range = getRange(freq);
// Save the highest magnitude and corresponding frequency:
if (mag > magnitudes[range])
{
magnitudes[range] = mag;
frequencies[range] = freq;
}
}
var threshold = 0.9 * (magnitudes[0] + magnitudes[1] + magnitudes[2] +
magnitudes[3] + magnitudes[4] + magnitudes[5]) / 6;
for (int i = 0; i < 6; i++)
{
if (magnitudes[i] > threshold)
{
spectrum.Add(new SpectrumPoint()
{
Freq = frequencies[i], Time = time
});
}
}
}
private static void LinqVersion()
{
byte[] outputBuffer = new byte[1024 * 4];
var filter =
LoadBlocks(INPUT_FILENAME, outputBuffer.Length)
.Select(cb => Fourier.FFT(cb, FourierDirection.Forward))
.Select(cb => LowPassFilter(cb))
.Select(cb => Fourier.FFT(cb, FourierDirection.Backward));
using (Stream output = File.Create(LINQ_OUTPUT_FILENAME))
{
foreach (ComplexF[] complexBuffer in filter)
{
WriteComplexBuffer(output, complexBuffer);
}
}
}
public float[] FFTForward(float[] signal, int startIndex, int length)
{
float[] complexSignal = new float[2 * length]; /*even - Re, odd - Img, thats how Exocortex works*/
// take 371 ms each 11.6 ms (2048 samples each 64 samples)
for (int i = 0; i < length /*2048*/; i++)
{
complexSignal[2 * i] = signal[startIndex + i];
complexSignal[(2 * i) + 1] = 0;
}
lock (lockObject)
{
Fourier.FFT(complexSignal, length, FourierDirection.Forward);
}
return(complexSignal);
}
/// <summary>
/// Generate a spectrogram array spaced linearily
/// </summary>
/// <param name="samples">audio data</param>
/// <param name="fftWindowsSize">fft window size</param>
/// <param name="fftOverlap">overlap in number of samples (normaly half of the fft window size) [low number = high overlap]</param>
/// <returns>spectrogram jagged array</returns>
public static double[][] CreateSpectrogramExocortex(float[] samples, int fftWindowsSize, int fftOverlap)
{
int numberOfSamples = samples.Length;
// overlap must be an integer smaller than the window size
// half the windows size is quite normal
double[] windowArray = FFTWindow.GetWindowFunction(FFTWindowType.HANNING, fftWindowsSize);
// width of the segment - e.g. split the file into X time slots (numberOfSegments) and do analysis on each slot
int numberOfSegments = (numberOfSamples - fftWindowsSize) / fftOverlap;
var frames = new double[numberOfSegments][];
// even - Re, odd - Img
var complexSignal = new float[2 * fftWindowsSize];
for (int i = 0; i < numberOfSegments; i++)
{
// apply Hanning Window
for (int j = 0; j < fftWindowsSize; j++)
{
// Weight by Hann Window
complexSignal[2 * j] = (float)(windowArray[j] * samples[i * fftOverlap + j]);
// need to clear out as fft modifies buffer (phase)
complexSignal[2 * j + 1] = 0;
}
// FFT transform for gathering the spectrum
Fourier.FFT(complexSignal, fftWindowsSize, FourierDirection.Forward);
// get the ABS of the complex signal
var band = new double[fftWindowsSize / 2];
for (int j = 0; j < fftWindowsSize / 2; j++)
{
double re = complexSignal[2 * j];
double img = complexSignal[2 * j + 1];
band[j] = (double)Math.Sqrt(re * re + img * img);
}
frames[i] = band;
}
return(frames);
}
/// <summary>
/// Create spectrogram of the input file
/// </summary>
/// <param name = "proxy">Proxy used to read from file</param>
/// <param name = "filename">Filename</param>
/// <param name = "milliseconds">Milliseconds to process</param>
/// <param name = "startmilliseconds">Starting point of the processing</param>
/// <returns>Spectrogram</returns>
public float[][] CreateSpectrogram(IWaveformPlayer proxy, string filename, int milliseconds, int startmilliseconds, bool doNormalise)
{
//read 5512 Hz, Mono, PCM, with a specific proxy
float[] samples = BassProxy.ReadMonoFromFile(filename, SampleRate, milliseconds, startmilliseconds);
if (doNormalise)
{
NormalizeInPlace(samples);
}
int overlap = Overlap;
int fftWindowsSize = WdftSize; // aka N = FFT Length
double[] windowArray = FFTWindow.GetWindowFunction(FFTWindowType.HANNING, fftWindowsSize);
int width = (samples.Length - fftWindowsSize) / overlap; /*width of the image*/
float[][] frames = new float[width][];
float[] complexSignal = new float[2 * fftWindowsSize]; /*even - Re, odd - Img*/
for (int i = 0; i < width; i++)
{
//take 371 ms each 11.6 ms (2048 samples each 64 samples)
// apply Hanning Window
for (int j = 0; j < fftWindowsSize /*2048*/; j++)
{
// Weight by Hann Window
complexSignal[2 * j] = (float)(windowArray[j] * samples[i * overlap + j]);
//complexSignal[2*j] = (float) ((4.0/(fftWindowsSize - 1)) * windowArray[j]*samples[i*overlap + j]); /*Weight by Hann Window*/
complexSignal[2 * j + 1] = 0; // need to clear out as fft modifies buffer
}
//FFT transform for gathering the spectrum
Fourier.FFT(complexSignal, fftWindowsSize, FourierDirection.Forward);
float[] band = new float[fftWindowsSize / 2 + 1];
for (int j = 0; j < fftWindowsSize / 2 + 1; j++)
{
double re = complexSignal[2 * j];
double img = complexSignal[2 * j + 1];
//double img = 0.0; // TODO: Zero out the imaginary component (phase) ? / need to clear out as fft modifies buffer
band[j] = (float)Math.Sqrt(re * re + img * img);
}
frames[i] = band;
}
return(frames);
}
/// <summary>
/// Applies a Notch Filter to a Signal
/// </summary>
/// <param name="s">The Signal to filter</param>
/// <returns>The Filtered Signal</returns>
public static Signal Notch(Signal s)
{
Signal fft = Fourier.FFT(s);
Signal result = new Signal(s.Length);
for (int i = 0; i < result.Length; i++)
{
result[i] = fft[i];
}
for (int i = 9; i < 16; i++)
{
result[i] = 0;
}
for (int i = 497; i < 504; i++)
{
result[i] = 0;
}
return(Fourier.InverseFFT(result));
}
public void GetPower()
{
int count = WINDOW_SIZE / FLOAT_SIZE;
complex = new Complex[count];
real = new double[count];
imag = new double[count];
for (int i = 0; i < WINDOW_SIZE; i += 4)
{
//audioStream.Read(buffer, 0, FLOAT_SIZE);
complex[i] = (Complex)BitConverter.ToSingle(window, i);
}
Fourier.FFT(complex, count, FourierDirection.Forward);
real = complex.Select(c => Math.Pow(c.Re, 2)).ToArray();
imag = complex.Select(c => Math.Pow(c.Im, 2)).ToArray();
audioStream.Position -= (WINDOW_SIZE - WINDOW_SHIFT_SIZE);
audioStream.Read(window, 0, WINDOW_SIZE);
}
private static void SequentialVersion()
{
using (Stream output = File.Create(OUTPUT_FILENAME))
{
byte[] outputBuffer = new byte[1024 * 4];
// Stage 1
foreach (ComplexF[] complexBuffer in LoadBlocks(INPUT_FILENAME, outputBuffer.Length))
{
// Stage 2
ComplexF[] result = Fourier.FFT(complexBuffer, FourierDirection.Forward);
// Stage 3
result = LowPassFilter(result);
// Stage 4
result = Fourier.FFT(result, FourierDirection.Backward);
// Stage 5
WriteComplexBuffer(output, result);
}
}
}
public static double[] padded_IFFT(Complex[] complexSignal)
{
int N = MathUtils.NextPowerOfTwo(complexSignal.Length);
if (N <= 4096)
{
complexSignal = zeros(complexSignal, N);
Fourier.FFT(complexSignal, N, FourierDirection.Backward);
}
else
{
N = 4096;
Array.Resize(ref complexSignal, N);
Fourier.FFT(complexSignal, N, FourierDirection.Backward);
}
// get the result
double[] fft_real = new double[N];
for (int j = 0; j < N; j++)
{
fft_real[j] = complexSignal[j].Re;
}
return(fft_real);
}
private static void CalculateTremor()
{
GetAccelAmplitudes();
Fourier.FFT(accelAmplitudes, FourierDirection.Forward);
float[] fftAmplitudes = GetFFTAmplitudes(accelAmplitudes);
float[] binFrequencies = new float[(int)(fftAmplitudes.Length)];
float halfSampleFreq = Sample_Frequency / 2;
float freq = 0, maxAmp = Threshold_Amplitude;
for (int binIndex = 0; binIndex < binFrequencies.Length; binIndex++)
{
freq = binIndex * halfSampleFreq / fftAmplitudes.Length; // From: (bin_id * freq/2) / N
binFrequencies[binIndex] = freq;
if (fftAmplitudes[binIndex] > maxAmp && freq > Threshold_Frequency)
{
maxAmp = fftAmplitudes[binIndex];
Amplitude = maxAmp;
Frequency = freq;
}
}
DataLogging.LogProcessedData(binFrequencies, fftAmplitudes);
}
public Bitmap Draw()
{
var _bitmap = bitmap.Clone() as Bitmap;
return(Fourier.FFT(_bitmap));
}
private static double WeightedVolume2(SoundBuffer src, double dbSPL, double dbSPLBase)
{
double v = 0;
uint sr = src.SampleRate;
// Read buffer into array of complex
Complex[][] data = src.ToComplexArray();
// We only have a single channel
Complex[] cdata = data[0];
// FFT in place
Fourier.FFT(cdata.Length, cdata);
// Calculate magnitude, weighted by 80-phon loudness, for each loudness band.
// These are the ISO measured points:
FilterProfile lfg;
if (dbSPLBase == 0)
{
lfg = SPL(dbSPL);
}
else
{
lfg = DifferentialSPL(dbSPL, dbSPLBase);
}
// lfg.Add(new FreqGain(sr / 2, lfg[lfg.Count - 1].Gain));
// Cover the ISO measured range (only...)
int nStart = (int)(lfg[0].Freq * (long)cdata.Length / sr);
int nEnd = (int)(lfg[lfg.Count - 1].Freq * (long)cdata.Length / sr);
// Just use linear interpolation (on a dB scale; linear freq scale) of gain between each measured point
int nfg = 0;
int startp = nStart;
int endp = (int)(lfg[nfg + 1].Freq * (long)cdata.Length / sr); // endpoint of this band
double dB1 = lfg[nfg].Gain; // SPL of the ISO223 curve at this freq
double dB2 = lfg[nfg + 1].Gain; // ...and the next point
double vThisBand = 0;
int nThisBand = 0;
for (int j = nStart; j < nEnd; j++)
{
if (j > endp)
{
if (nThisBand > 0)
{
v += Math.Sqrt(vThisBand / nThisBand); // RMS
}
while (j >= endp)
{
nfg++;
startp = j;
endp = (int)(lfg[nfg + 1].Freq * (long)cdata.Length / sr);
dB1 = lfg[nfg].Gain;
dB2 = lfg[nfg + 1].Gain;
}
vThisBand = 0;
nThisBand = 0;
}
Complex c = cdata[j];
double dbHere = dB1 + ((dB2 - dB1) * (double)(j - startp) / (double)(endp - startp));
vThisBand += (c.Re * c.Re) / MathUtil.gain(dbHere);
nThisBand++;
}
if (nThisBand > 0)
{
v += Math.Sqrt(vThisBand / nThisBand);
}
return(v);
}
private bool ComputePartitionedImpulseFFT()
{
if (_impulse == null)
{
return(false);
}
ushort nChannels;
if (_input == null)
{
nChannels = _impulse.NumChannels;
}
else
{
nChannels = _input.NumChannels;
}
ushort p;
int N = _impulseLength;
int Nh = N << 1;
int K = (int)(N / _partitions);
int L = MathUtil.NextPowerOfTwo(K << 1);
int P = (int)Math.Ceiling((double)N / (double)K);
// Initialize the arrays of impulse-FFTs
_PartitionedImpulseFFT = new Complex[nChannels][][];
Complex[][] tmp = new Complex[nChannels][];
for (ushort c = 0; c < nChannels; c++)
{
tmp[c] = new Complex[Nh];
_PartitionedImpulseFFT[c] = new Complex[P][];
for (p = 0; p < P; p++)
{
_PartitionedImpulseFFT[c][p] = new Complex[L];
}
}
// Read all samples from the impulse, & fft in blocks of size K (padded to L, L~=2K)
int i = 0;
int n = 0;
p = 0;
foreach (ISample sample in _impulse)
{
// Reading a segment of the impulse (into src[])
n++;
if (n > _impulseLength)
{
break;
} // source gave us more data than we'd expected
for (ushort c = 0; c < _impulse.NumChannels; c++)
{
tmp[c][i + K] = new Complex(sample[c], 0); // TBD
}
i++;
if (i >= K)
{
// Do the fft of this segment of the impulse, into fftImpulse[]
for (ushort c = 0; c < nChannels; c++)
{
if (c >= _impulse.NumChannels)
{
// Handle the case where impulse has only one channel, but input (hence nChannels) is wider;
// we already computed the fft in _PartitionedImpulseFFT[0][p], so just duplicate it.
// In fact we can just ref the whole buffer -- no need even to make a deep copy
_PartitionedImpulseFFT[c][p] = _PartitionedImpulseFFT[0][p];
}
else
{
Array.Copy(tmp[c], _PartitionedImpulseFFT[c][p], L);
Fourier.FFT(L, _PartitionedImpulseFFT[c][p]);
}
}
i = 0; p++;
}
}
_impulseLengthOrig = n - 1;
if (p < P)
{
// Fill any extra space with nulls
for (int ii = i; ii < K; ii++)
{
for (ushort c = 0; c < _impulse.NumChannels; c++)
{
tmp[c][ii + K] = new Complex(0, 0); // TBD
}
}
// FFT the last segment
for (ushort c = 0; c < nChannels; c++)
{
if (c >= _impulse.NumChannels)
{
// Handle the case where impulse has only one channel, but input (hence nChannels) is wider;
// we already computed the fft in _PartitionedImpulseFFT[0][p], so just duplicate it.
// In fact we can just ref the whole buffer -- no need even to make a deep copy
_PartitionedImpulseFFT[c][p] = _PartitionedImpulseFFT[0][p];
}
else
{
Array.Copy(tmp[c], _PartitionedImpulseFFT[c][p], L);
Fourier.FFT(L, _PartitionedImpulseFFT[c][p]);
}
}
}
return(true);
}
private bool ComputeNormalImpulseFFT()
{
if (_impulse == null)
{
return(false);
}
ushort nChannels;
if (_input == null)
{
nChannels = _impulse.NumChannels;
}
else
{
nChannels = _input.NumChannels;
}
int N = _impulseLength;
int Nh = N << 1;
// Initialize the array of impulse-FFTs
_NormalImpulseFFT = new Complex[nChannels][];
for (ushort c = 0; c < nChannels; c++)
{
_NormalImpulseFFT[c] = new Complex[Nh];
}
// Read all samples from the impulse
// into the second half of the src buffer (the first half is all zeros)
int i = 0;
foreach (ISample sample in _impulse)
{
for (ushort c = 0; c < _impulse.NumChannels; c++)
{
_NormalImpulseFFT[c][i + N] = new Complex(sample[c], 0);
}
i++;
if (i >= _impulseLength)
{
break;
} // source gave us more data than we'd expected
}
_impulseLengthOrig = i - 1;
// Do the fft of impulse, in-place
for (ushort c = 0; c < nChannels; c++)
{
if (c >= _impulse.NumChannels)
{
// Handle the case where impulse has only one channel, but input (hence nChannels) is wider;
// we already computed the fft in _NormalImpulseFFT[0], so just duplicate it.
// In fact we can just ref the whole buffer -- no need even to make a deep copy
_NormalImpulseFFT[c] = _NormalImpulseFFT[0];
}
else
{
Fourier.FFT(Nh, _NormalImpulseFFT[c]);
}
}
return(true);
}
/// <summary>
/// 读取样本
/// </summary>
/// <param name="transmit"></param>
private void ReadSample(bool transmit)
{
// 提取数据， sampleIndex特定位置
clip.GetData(sampleBuffer, sampleIndex);
// 抓住一个新的样品缓冲区
float[] targetSampleBuffer = VoiceChatFloatPool.Instance.Get();
// 将我们的真实样本重新取样到缓冲区中
Resample(sampleBuffer, targetSampleBuffer);
// Forward index
sampleIndex += recordSampleSize;
// 最高自动检测频率
float freq = float.MinValue;
int index = -1;
// 自动检测语音，但如果我们要按键传输，就不需要检测了
if (autoDetectSpeaking && !transmit)
{
// 清除FFT缓冲区
for (int i = 0; i < fftBuffer.Length; ++i)
{
fftBuffer[i] = 0;
}
// 复制到FFT缓冲区
Array.Copy(targetSampleBuffer, 0, fftBuffer, 0, targetSampleBuffer.Length);
// 应用FFT
Fourier.FFT(fftBuffer, fftBuffer.Length / 2, FourierDirection.Forward);
// 获取最高频率
for (int i = 0; i < fftBuffer.Length; ++i)
{
if (fftBuffer[i] > freq)
{
freq = fftBuffer[i];
index = i;
}
}
}
// 如果我们有一个事件，并且
if (NewSample != null && (transmit || forceTransmit > 0 || index >= autoDetectIndex))
{
// 如果我们自动检测到声音，就强行录音一会儿
if (index >= autoDetectIndex)
{
if (forceTransmit <= 0)
{
while (previousSampleBuffer.Count > 0)
{
TransmitBuffer(previousSampleBuffer.Remove());
}
}
forceTransmit = forceTransmitTime;
}
TransmitBuffer(targetSampleBuffer);
}
else
{
if (previousSampleBuffer.Count == previousSampleBuffer.Capacity)
{
VoiceChatFloatPool.Instance.Return(previousSampleBuffer.Remove());
}
previousSampleBuffer.Add(targetSampleBuffer);
}
}
private Complex[] GetFourier(double[] Input)
{
Complex[] FourierMass = Fourier.FFT(Input);
return(FourierMass);
}
/// <summary>
/// Compute a 1D real-symmetric fast fourier transform.
/// </summary>
/// <param name="data"></param>
/// <param name="length"></param>
/// <param name="direction"></param>
public static void RFFT(float[] data, int length, FourierDirection direction)
{
if (data == null)
{
throw new ArgumentNullException("data");
}
if (data.Length < length)
{
throw new ArgumentOutOfRangeException("length", length, "must be at least as large as 'data.Length' parameter");
}
if (Fourier.IsPowerOf2(length) == false)
{
throw new ArgumentOutOfRangeException("length", length, "must be a power of 2");
}
float c1 = 0.5f, c2;
float theta = (float)Math.PI / (length / 2);
if (direction == FourierDirection.Forward)
{
c2 = -0.5f;
FFT(data, length / 2, direction);
}
else
{
c2 = 0.5f;
theta = -theta;
}
float wtemp = (float)Math.Sin(0.5 * theta);
float wpr = -2 * wtemp * wtemp;
float wpi = (float)Math.Sin(theta);
float wr = 1 + wpr;
float wi = wpi;
// do / undo packing
for (int i = 1; i < length / 4; i++)
{
int a = 2 * i;
int b = length - 2 * i;
float h1r = c1 * (data[a] + data[b]);
float h1i = c1 * (data[a + 1] - data[b + 1]);
float h2r = -c2 * (data[a + 1] + data[b + 1]);
float h2i = c2 * (data[a] - data[b]);
data[a] = h1r + wr * h2r - wi * h2i;
data[a + 1] = h1i + wr * h2i + wi * h2r;
data[b] = h1r - wr * h2r + wi * h2i;
data[b + 1] = -h1i + wr * h2i + wi * h2r;
wr = (wtemp = wr) * wpr - wi * wpi + wr;
wi = wi * wpr + wtemp * wpi + wi;
}
if (direction == FourierDirection.Forward)
{
float hir = data[0];
data[0] = hir + data[1];
data[1] = hir - data[1];
}
else
{
float hir = data[0];
data[0] = c1 * (hir + data[1]);
data[1] = c1 * (hir - data[1]);
Fourier.FFT(data, length / 2, direction);
}
}
static void Main()
{
//var A1 = new double[Nx];
//var A2 = new double[Nx];
//for (int i = 0; i < Nx; i++)
//{
// A1[i] = i;
// A2[i] = A1[i];
//}
//A2[Nx - 1] = 253;
//double res = NormsComparison(A2, A1);
int jj = 0;
SetPumping(r);
SetLosses(sigma);
SetSpaceFrequency();
SetInitials();
SetExtraConstants();
for (int i = 0; i < Nx; i++)
{
Console.WriteLine(E[i].Abs * E[i].Abs);
}
sumtimer.Start();
for (int q = 0; q < Nt; q++)
{
ffttimer.Start();
Ef = Fourier.FFT(E);
Pf = Fourier.FFT(P);
ffttimer.Stop();
for (int i = 0; i < Nx; i++)
{
Dfl[i] = exp1 * D[i];
Pfl[i] = exp2 * P[i];
Pf[i] = Pf[i] * exp4[i];
Efl[i] = exp2 * Pf[i] + exp3[i] * (Ef[i] - Pf[i]);
}
ffttimer.Start();
Efl = Fourier.IFFT(Efl);
ffttimer.Stop();
// Start iteration process
Set(E, Ec);
Set(P, Pc);
Set(D, Dc);
Set(E, Ej);
ffttimer.Start();
Ef = Fourier.FFT(Efl);
ffttimer.Stop();
for (int i = 0; i < Nx; i++)
{
exp5[i] = D[i] * E[i];
exp6[i] = 2 * R[i] - 0.5 * (Ec[i].Re * Pc[i].Re + Pc[i].Im * Ec[i].Im);
}
//jj = 0;
for (int j = 0; j < Nmax; j++)
{
//jj++;
for (int i = 0; i < Nx; i++)
{
Pj[i] = Pfl[i] + (exp5[i] + Dc[i] * Ec[i]) / 2 * dt;
Dj[i] = Dfl[i] + gamma * dt * (exp6[i] - 0.5 * (Ec[i].Re * Pc[i].Re + Pc[i].Im * Ec[i].Im)) / 2;
}
ffttimer.Start();
Pf = Fourier.FFT(Pj);
ffttimer.Stop();
for (int i = 0; i < Nx; i++)
{
Pf[i] = Pf[i] * exp4[i];
Dj[i] = Dj[i] * exp1;
Pj[i] = Pj[i] * exp2;
Ej[i] = exp2 * Pf[i] + exp3[i] * (Ef[i] - Pf[i]);
}
ffttimer.Start();
Ej = Fourier.IFFT(Ej);
ffttimer.Stop();
normtimer.Start();
double normE = NormsComparison(Ej, Ec);
double normP = NormsComparison(Pj, Pc);
double normD = NormsComparison(Dj, Dc);
if (normE < eps && normP < eps && normD < eps)
{
Ec = Ej;
Pc = Pj;
Dc = Dj;
normtimer.Stop();
if (jj != j)
{
Console.WriteLine(j);
jj = j;
}
break;
}
else
{
Set(Ej, Ec);
Set(Pj, Pc);
Set(Dj, Dc);
}
normtimer.Stop();
}
//Console.WriteLine(jj);
E = Ec;
P = Pc;
D = Dc;
}
sumtimer.Stop();
Console.WriteLine("Sum time: " + sumtimer.ElapsedMilliseconds / Nt);
Console.WriteLine("FFT time: " + ffttimer.ElapsedMilliseconds / Nt);
Console.WriteLine("Norm time: " + normtimer.ElapsedMilliseconds / Nt);
for (int i = 0; i < Nx; i++)
{
Console.WriteLine(E[i].Abs * E[i].Abs);
}
Console.ReadKey();
}
public static Trace[] Deconvolve(Trace[] data, double decA = Double.NegativeInfinity, bool onlyRe = false)
{
var tracesDataDeconv = new Trace[numberOfSonograms * numberOfTraces];
if (Double.IsNegativeInfinity(decA))
{
decA = curDecA;
}
var impulseSpec = new Exocortex.DSP.Complex[4096];
for (int i = 0; i < signal.Length; i++)
{
impulseSpec[i].Re = signal[i];
}
Exocortex.DSP.Fourier.FFT(impulseSpec, 4096, Exocortex.DSP.FourierDirection.Forward);
var impMulti = new Exocortex.DSP.Complex[4096];
if (decA >= 0)
{
var impulseSpecInv = new Exocortex.DSP.Complex[4096];
for (int i = 0; i < impulseSpec.Length; i++)
{
impulseSpecInv[i].Re = impulseSpec[i].Re;
impulseSpecInv[i].Im = -impulseSpec[i].Im;
}
for (int i = 0; i < impulseSpec.Length; i++)
{
impMulti[i] = impulseSpecInv[i] / (Math.Pow(impulseSpec[i].Re, 2) + Math.Pow(impulseSpec[i].Im, 2) + Math.Pow(decA, 2));
}
}
else
{
for (int i = 0; i < impulseSpec.Length; i++)
{
impMulti[i] = impulseSpec[i];
}
}
Parallel.For(0, data.Length, tn =>
{
var t = data[tn];
var tempColumn = new Exocortex.DSP.Complex[4096];
for (int i = 0; i < Math.Min(4096, maxtime); i++)
{
tempColumn[i].Re = t.data[i];
}
Fourier.FFT(tempColumn, 4096, FourierDirection.Forward);
for (int i = 0; i < 4096; i++)
{
tempColumn[i] = tempColumn[i] * impMulti[i];
}
Fourier.FFT(tempColumn, 4096, FourierDirection.Backward);
Trace tConv = new Trace(t);
for (int i = 0; i < Math.Min(4096, maxtime); i++)//обрезаем то, что вылезло за границы
{
tConv.data[i] = (onlyRe ? (float)tempColumn[i].Re : (float)Math.Sqrt(tempColumn[i].Re * tempColumn[i].Re + tempColumn[i].Im * tempColumn[i].Im)) / 4096;
}
tracesDataDeconv[tn] = tConv;
});
return(tracesDataDeconv);
}
public void Calculate()
{
try
{
sumtimer.Reset();
ffttimer.Reset();
normtimer.Reset();
if (OnCalculationStart != null)
{
OnCalculationStart(this, new EventArgs());
}
sumtimer.Start();
SetParameters();
for (int q = 0; q < Nt; q++)
{
if (CalculationCancelled)
{
break;
}
ffttimer.Start();
Ef = Fourier.FFT(E);
Pf = Fourier.FFT(P);
ffttimer.Stop();
for (int i = 0; i < Nx; i++)
{
Dfl[i] = exp1 * D[i];
Pfl[i] = exp2 * P[i];
Pf[i] = Pf[i] * exp4[i];
Efl[i] = exp2 * Pf[i] + exp3[i] * (Ef[i] - Pf[i]);
}
ffttimer.Start();
Efl = Fourier.IFFT(Efl);
ffttimer.Stop();
// Start iteration process
Set(E, Ec);
Set(P, Pc);
Set(D, Dc);
Set(E, Ej);
ffttimer.Start();
Ef = Fourier.FFT(Efl);
ffttimer.Stop();
for (int i = 0; i < Nx; i++)
{
exp5[i] = D[i] * E[i];
exp6[i] = 2 * R[i] - 0.5 * (Ec[i].Re * Pc[i].Re + Pc[i].Im * Ec[i].Im);
}
//jj = 0;
for (int j = 0; j < Nmax; j++)
{
//jj++;
for (int i = 0; i < Nx; i++)
{
Pj[i] = Pfl[i] + (exp5[i] + Dc[i] * Ec[i]) / 2 * dt;
Dj[i] = Dfl[i] + gamma * dt * (exp6[i] - 0.5 * (Ec[i].Re * Pc[i].Re + Pc[i].Im * Ec[i].Im)) / 2;
}
ffttimer.Start();
Pf = Fourier.FFT(Pj);
ffttimer.Stop();
for (int i = 0; i < Nx; i++)
{
Pf[i] = Pf[i] * exp4[i];
Dj[i] = Dj[i] * exp1;
Pj[i] = Pj[i] * exp2;
Ej[i] = exp2 * Pf[i] + exp3[i] * (Ef[i] - Pf[i]);
}
ffttimer.Start();
Ej = Fourier.IFFT(Ej);
ffttimer.Stop();
normtimer.Start();
//double normE = NormsComparison(Ej, Ec);
//double normP = NormsComparison(Pj, Pc);
//double normD = NormsComparison(Dj, Dc);
if (NormsComparison(Ej, Ec) < eps && NormsComparison(Pj, Pc) < eps && NormsComparison(Dj, Dc) < eps)
{
Ec = Ej;
Pc = Pj;
Dc = Dj;
normtimer.Stop();
if (jj != j)
{
Console.WriteLine(j);
jj = j;
}
break;
}
else
{
Set(Ej, Ec);
Set(Pj, Pc);
Set(Dj, Dc);
}
normtimer.Stop();
}
//Console.WriteLine(jj);
E = Ec;
P = Pc;
D = Dc;
}
}
catch (Exception ex)
{
if (OnCalculationError != null)
{
OnCalculationError(this, new EventArgs());
}
}
sumtimer.Stop();
if (OnCalculationFinish != null)
{
OnCalculationFinish(this, new EventArgs());
}
CalculationCancelled = false;
}