// reduce feature size by applying a pca
private float[] framePca(float[] procBuffer)
{
// substract pca mean values from data
LinearAlgebraUtils.VecSub(procBuffer, pcaMean, pcaMeanDims[0]);
// dot multiply transposed component matrix with data vector (comp * vec.T)
return(LinearAlgebraUtils.MatVecDot(pcaComp, procBuffer, pcaCompDims));
}
// transform a data buffer into its magnitude spectrum
public void transform(float[] dataRe, int n)
{
/* windowing */
float[] window = fftWin;
// create custom window for smaller segment processing
if (n < winLen)
{
window = hannWindow(n);
}
// multiply data buffer with window
LinearAlgebraUtils.VecMul(dataRe, window, n, 0);
// define temporary buffer for complex calculations
float[] dataIm = new float[winLen];
// calculate fourier transform
complexFourier(dataRe, dataIm);
// get absolute real magnitude spectrum and extrema
for (int i = 0; i <= winLen / 2 - simdLenFp32; i += simdLenFp32)
{
Vector <float> vecDataRe = new Vector <float>(dataRe, i);
Vector <float> vecDataIm = new Vector <float>(dataIm, i);
Vector.SquareRoot(vecDataRe * vecDataRe + vecDataIm * vecDataIm).CopyTo(dataRe, i);
}
dataRe[winLen / 2] = (float)Math.Sqrt(dataRe[winLen / 2] * dataRe[winLen / 2] + dataIm[winLen / 2] * dataIm[winLen / 2]);
Array.Clear(dataRe, winLen / 2 + 1, winLen / 2 - 1);
// scale magnitudes between 0 and 1
LinearAlgebraUtils.VecMinMax(dataRe, dataRe.Min(), dataRe.Max(), winLen / 2 + 1);
}
public PitchClassifier(string w1File, string w2File, string b1File, string b2File)
{
// load weights and biases from files
LinearAlgebraUtils.loadNdarray(w1File, out w1, out w1Dims);
LinearAlgebraUtils.loadNdarray(w2File, out w2, out w2Dims);
LinearAlgebraUtils.loadNdarray(b1File, out b1, out b1Dims);
LinearAlgebraUtils.loadNdarray(b2File, out b2, out b2Dims);
}
// get the pitch probabilities for the feature array
public float[] predictProb(float[] features)
{
float[] l1 = LinearAlgebraUtils.MatVecDot(w1, features, w1Dims);
LinearAlgebraUtils.VecAdd(l1, b1, b1Dims[0]);
LinearAlgebraUtils.VecReLu(l1, w1Dims[0]);
float[] l2 = LinearAlgebraUtils.MatVecDot(w2, l1, w2Dims);
LinearAlgebraUtils.VecAdd(l2, b2, b2Dims[0]);
return(softMax(l2, w2Dims[0]));
}
public AudioPreprocessor(string pcaMeanFile, string pcaCompFile)
{
// load pca mean vector
LinearAlgebraUtils.loadNdarray(pcaMeanFile, out pcaMean, out pcaMeanDims);
// load pca component matrix
LinearAlgebraUtils.loadNdarray(pcaCompFile, out pcaComp, out pcaCompDims);
// init magnitude spectrum transformer
magnitudeSpectrum = new MagnitudeSpectrum(WinLen);
}
// uses the complex fft to transform two signals simultaniously
public void stereoTransform(float[] dataRe, float[] dataIm, int n)
{
/* windowing */
float[] window = fftWin;
// create custom window for smaller segment processing
if (n < winLen)
{
window = hannWindow(n);
}
// multiply data buffer with window
LinearAlgebraUtils.VecMul(dataRe, window, n, 0);
LinearAlgebraUtils.VecMul(dataIm, window, n, 0);
// calculate fourier transform
complexFourier(dataRe, dataIm);
// split combined spectrum into magnitude signal spectra
float aRe = 0, aIm = 0, bRe = 0, bIm = 0;
float tmpRe = dataRe[winLen / 2 - 1];
float tmpIm = dataIm[winLen / 2 - 1];
for (int i = 1; i < winLen / 2; i++)
{
aRe = dataRe[i] + dataRe[winLen - i];
aIm = dataIm[i] - dataIm[winLen - i];
bRe = dataIm[i] + dataIm[winLen - i];
bIm = dataRe[winLen - i] - dataRe[i];
dataRe[i] = (float)Math.Sqrt(aRe * aRe + aIm * aIm);
dataIm[i] = (float)Math.Sqrt(bRe * bRe + bIm * bIm);
}
aRe = dataRe[winLen / 2] + tmpRe;
aIm = dataIm[winLen / 2] - tmpIm;
bRe = dataIm[winLen / 2] + tmpIm;
bIm = tmpRe - dataRe[winLen / 2];
dataRe[winLen / 2] = (float)Math.Sqrt(aRe * aRe + aIm * aIm);
dataIm[winLen / 2] = (float)Math.Sqrt(bRe * bRe + bIm * bIm);
// set unused buffer parts to zero
Array.Clear(dataRe, winLen / 2 + 1, winLen / 2 - 1);
Array.Clear(dataIm, winLen / 2 + 1, winLen / 2 - 1);
// scale magnitudes between 0 and 1
LinearAlgebraUtils.VecMinMax(dataRe, dataRe.Min(), dataRe.Max(), winLen / 2 + 1);
LinearAlgebraUtils.VecMinMax(dataIm, dataIm.Min(), dataIm.Max(), winLen / 2 + 1);
}
