/// <summary>
/// <para>Computes S(implified)PNCC vector in one frame according to [Kim and Stern, 2016].</para>
/// <para>
/// General algorithm:
/// <list type="number">
/// <item>Apply window</item>
/// <item>Obtain power spectrum</item>
/// <item>Apply gammatone filters (squared)</item>
/// <item>Mean power normalization</item>
/// <item>Apply nonlinearity</item>
/// <item>Do DCT-II (normalized)</item>
/// </list>
/// </para>
/// </summary>
/// <param name="block">Block of data</param>
/// <param name="features">Features (one SPNCC feature vector) computed in the block</param>
public override void ProcessFrame(float[] block, float[] features)
{
const float meanPower = 1e10f;
// 0) base extractor applies window
// 1) calculate power spectrum
_fft.PowerSpectrum(block, _spectrum, false);
// 2) apply gammatone filterbank
FilterBanks.Apply(FilterBank, _spectrum, _filteredSpectrum);
// 3) mean power normalization:
var sumPower = 0.0f;
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
sumPower += _filteredSpectrum[j];
}
_mean = LambdaMu * _mean + (1 - LambdaMu) * sumPower;
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
_filteredSpectrum[j] *= meanPower / _mean;
}
// 4) nonlinearity (pow ^ d or Log10)
if (_power != 0)
{
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
_filteredSpectrum[j] = (float)Math.Pow(_filteredSpectrum[j], 1.0 / _power);
}
}
else
{
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
_filteredSpectrum[j] = (float)Math.Log10(_filteredSpectrum[j] + float.Epsilon);
}
}
// 5) dct-II (normalized)
_dct.DirectNorm(_filteredSpectrum, features);
// 6) (optional) replace first coeff with log(energy)
if (_includeEnergy)
{
features[0] = (float)Math.Log(Math.Max(block.Sum(x => x * x), _logEnergyFloor));
}
}
public void TestDct2Norm()
{
float[] res = new float[6];
float[] resDct2 = { 0.91923882f, -0.11214018f, 0.35370055f, -0.30289775f, 0.49497475f, 0.18332565f };
var dct2 = new Dct2(8);
dct2.DirectNorm(_test, res);
Assert.That(res, Is.EqualTo(resDct2).Within(1e-5));
}
/// <summary>
/// S(implified)PNCC algorithm according to [Kim & Stern, 2016].
/// In each frame do:
///
/// 1) Apply window (if rectangular window was specified then just do nothing)
/// 2) Obtain power spectrum
/// 3) Apply gammatone filters (squared)
/// 4) Mean power normalization
/// 5) Apply nonlinearity
/// 6) Do dct-II (normalized)
///
/// </summary>
/// <param name="samples">Samples for analysis</param>
/// <returns>List of pncc vectors</returns>
public override float[] ProcessFrame(float[] block)
{
const float meanPower = 1e10f;
// fill zeros to fftSize if frameSize < fftSize
for (var k = FrameSize; k < block.Length; block[k++] = 0)
{
;
}
// 1) apply window
block.ApplyWindow(_windowSamples);
// 2) calculate power spectrum
_fft.PowerSpectrum(block, _spectrum, false);
// 3) apply gammatone filterbank
FilterBanks.Apply(FilterBank, _spectrum, _filteredSpectrum);
// 4) mean power normalization:
var sumPower = 0.0f;
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
sumPower += _filteredSpectrum[j];
}
_mean = LambdaMu * _mean + (1 - LambdaMu) * sumPower;
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
_filteredSpectrum[j] *= meanPower / _mean;
}
// 5) nonlinearity (pow ^ d or Log10)
if (_power != 0)
{
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
_filteredSpectrum[j] = (float)Math.Pow(_filteredSpectrum[j], 1.0 / _power);
}
}
else
{
for (var j = 0; j < _filteredSpectrum.Length; j++)
{
_filteredSpectrum[j] = (float)Math.Log10(_filteredSpectrum[j] + float.Epsilon);
}
}
// 6) dct-II (normalized)
var spnccs = new float[FeatureCount];
_dct.DirectNorm(_filteredSpectrum, spnccs);
return(spnccs);
}
