/// <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);
}
/// <summary>
/// Compute the sequence of feature vectors from some fragment of a signal
/// </summary>
/// <param name="samples">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(float[] samples, int startSample, int endSample)
{
Guard.AgainstInvalidRange(startSample, endSample, "starting pos", "ending pos");
var nullExtractorPos = _extractors.IndexOf(null);
if (nullExtractorPos >= 0)
{
throw new ArgumentException($"Unknown feature: {FeatureDescriptions[nullExtractorPos]}");
}
var featureVectors = new List <FeatureVector>();
var pitchPos = 0;
var i = startSample;
while (i + FrameSize < endSample)
{
// prepare all blocks in memory for the current step:
_zeroblock.FastCopyTo(_block, _fftSize);
samples.FastCopyTo(_block, FrameSize, i);
// apply window if necessary
if (_window != WindowTypes.Rectangular)
{
_block.ApplyWindow(_windowSamples);
}
// compute and prepare spectrum
_fft.MagnitudeSpectrum(_block, _spectrum);
// apply filterbank (ignoring 0th coefficient)
for (var k = 0; k < _filterbank.Length; k++)
{
_mappedSpectrum[k + 1] = 0.0f;
for (var j = 0; j < _spectrum.Length; j++)
{
_mappedSpectrum[k + 1] += _filterbank[k][j] * _spectrum[j];
}
}
// extract spectral features
var featureVector = new float[FeatureCount];
for (var j = 0; j < _extractors.Count; j++)
{
featureVector[j] = _extractors[j](_mappedSpectrum, _frequencies);
}
// ...and maybe harmonic features
if (_harmonicExtractors != null)
{
var pitch = _pitchTrack == null?_pitchEstimator(_spectrum) : _pitchTrack[pitchPos++];
_peaksDetector(_spectrum, _peaks, _peakFrequencies, SamplingRate, pitch);
var offset = _extractors.Count;
for (var j = 0; j < _harmonicExtractors.Count; j++)
{
featureVector[j + offset] = _harmonicExtractors[j](_spectrum, _peaks, _peakFrequencies);
}
}
// finally create new feature vector
featureVectors.Add(new FeatureVector
{
Features = featureVector,
TimePosition = (double)i / SamplingRate
});
i += HopSize;
}
return(featureVectors);
}
/// <summary>
/// Compute the sequence of feature vectors from some fragment of a signal
/// </summary>
/// <param name="samples">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(float[] samples, int startSample, int endSample)
{
Guard.AgainstInvalidRange(startSample, endSample, "starting pos", "ending pos");
var nullExtractorPos = _extractors.IndexOf(null);
if (nullExtractorPos >= 0)
{
throw new ArgumentException($"Unknown feature: {FeatureDescriptions[nullExtractorPos]}");
}
var featureVectors = new List <FeatureVector>();
var i = startSample;
while (i + FrameSize < endSample)
{
// prepare all blocks in memory for the current step:
_zeroblock.FastCopyTo(_block, _fftSize);
samples.FastCopyTo(_block, FrameSize, i);
// apply window if necessary
if (_window != WindowTypes.Rectangular)
{
_block.ApplyWindow(_windowSamples);
}
// compute and prepare spectrum
_fft.MagnitudeSpectrum(_block, _spectrum);
var featureVector = new float[FeatureCount];
if (_spectrum.Length == _frequencies.Length)
{
_mappedSpectrum = _spectrum;
}
else
{
for (var j = 0; j < _mappedSpectrum.Length; j++)
{
_mappedSpectrum[j] = _spectrum[_frequencyPositions[j]];
}
}
// extract spectral features
for (var j = 0; j < _extractors.Count; j++)
{
featureVector[j] = _extractors[j](_mappedSpectrum, _frequencies);
}
// finally create new feature vector
featureVectors.Add(new FeatureVector
{
Features = featureVector,
TimePosition = (double)i / SamplingRate
});
i += HopSize;
}
return(featureVectors);
}