public void TestSimpleNotFullStaticAverage()
{
var sma = new SimpleMovingAverage(5);
sma.Add(3);
sma.Add(4);
sma.Add(2);
sma.Add(3);
var average = sma.Average();
Assert.Equal(3, average);
}
public void TestSimpleNotFullStaticAverage()
{
var sma = new SimpleMovingAverage(5);
sma.Add(3);
sma.Add(4);
sma.Add(2);
sma.Add(3);
var average = sma.Average();
Assert.Equal(3, average);
}
public double CalculateVectorNormalizedAverage(WorkoutSampleVector vector)
{
if (!vector.HasData)
{
return(0);
}
if (vector.NumberOfSamples < 30)
{
return(CalculateVectorAverage(vector));
}
var movingAverageBuffer = new SimpleMovingAverage(30);
double movingAverage = 0;
var movingAverageSamples = 1;
movingAverageBuffer.Add(vector.Vector[0].dataPoint);
for (var i = 1; i < vector.NumberOfSamples; i++)
{
if (vector.Vector[i].dataPoint < 0)
{
continue;
}
var timeDiff = vector.Vector[i].timeOffsetSeconds - vector.Vector[i - 1].timeOffsetSeconds;
for (var j = 0; j < timeDiff; j++)
//Cannot multiply value across time in the sample as we need a 30s moving avg.
{
movingAverageBuffer.Add(vector.Vector[i].dataPoint);
if (movingAverageBuffer.NumberOfSamples >= 30)
{
movingAverage += Math.Pow(Math.Round(movingAverageBuffer.Average()), 4);
movingAverageSamples++;
}
}
}
var average = movingAverage / movingAverageSamples;
average = Math.Round(Math.Pow(average, 0.25));
return(average);
}
public void TestSimpleMovingAverage()
{
var sma = new SimpleMovingAverage(5);
sma.Add(10);
sma.Add(55);
sma.Add(33);
sma.Add(56);
sma.Add (88);
sma.Add(23);
var average = sma.Average();
Assert.Equal(51, average);
}
public void TestSimpleMovingAverage()
{
var sma = new SimpleMovingAverage(5);
sma.Add(10);
sma.Add(55);
sma.Add(33);
sma.Add(56);
sma.Add(88);
sma.Add(23);
var average = sma.Average();
Assert.Equal(51, average);
}
public void Generate(AudioTrack track)
{
IAudioStream audioStream = new ResamplingStream(
new MonoStream(AudioStreamFactory.FromFileInfoIeee32(track.FileInfo)),
ResamplingQuality.Medium, profile.SamplingRate);
STFT stft = new STFT(audioStream, profile.WindowSize, profile.HopSize, WindowType.Hann, STFT.OutputFormat.Decibel);
int index = 0;
int indices = stft.WindowCount;
int processedFrames = 0;
float[] spectrum = new float[profile.WindowSize / 2];
float[] smoothedSpectrum = new float[spectrum.Length - profile.SpectrumSmoothingLength + 1]; // the smooved frequency spectrum of the current frame
var spectrumSmoother = new SimpleMovingAverage(profile.SpectrumSmoothingLength);
float[] spectrumTemporalAverage = new float[spectrum.Length]; // a running average of each spectrum bin over time
float[] spectrumResidual = new float[spectrum.Length]; // the difference between the current spectrum and the moving average spectrum
var peakHistory = new PeakHistory(1 + profile.TargetZoneDistance + profile.TargetZoneLength, spectrum.Length / 2);
var peakPairs = new List <PeakPair>(profile.PeaksPerFrame * profile.PeakFanout); // keep a single instance of the list to avoid instantiation overhead
var subFingerprints = new List <SubFingerprint>();
while (stft.HasNext())
{
// Get the FFT spectrum
stft.ReadFrame(spectrum);
// Skip frames whose average spectrum volume is below the threshold
// This skips silent frames (zero samples) that only contain very low noise from the FFT
// and that would screw up the temporal spectrum average below for the following frames.
if (spectrum.Average() < spectrumMinThreshold)
{
index++;
continue;
}
// Smooth the frequency spectrum to remove small peaks
if (profile.SpectrumSmoothingLength > 0)
{
spectrumSmoother.Clear();
for (int i = 0; i < spectrum.Length; i++)
{
var avg = spectrumSmoother.Add(spectrum[i]);
if (i >= profile.SpectrumSmoothingLength)
{
smoothedSpectrum[i - profile.SpectrumSmoothingLength] = avg;
}
}
}
// Update the temporal moving bin average
if (processedFrames == 0)
{
// Init averages on first frame
for (int i = 0; i < spectrum.Length; i++)
{
spectrumTemporalAverage[i] = spectrum[i];
}
}
else
{
// Update averages on all subsequent frames
for (int i = 0; i < spectrum.Length; i++)
{
spectrumTemporalAverage[i] = ExponentialMovingAverage.UpdateMovingAverage(
spectrumTemporalAverage[i], profile.SpectrumTemporalSmoothingCoefficient, spectrum[i]);
}
}
// Calculate the residual
// The residual is the difference of the current spectrum to the temporal average spectrum. The higher
// a bin residual is, the steeper the increase in energy in that peak.
for (int i = 0; i < spectrum.Length; i++)
{
spectrumResidual[i] = spectrum[i] - spectrumTemporalAverage[i] - 90f;
}
// Find local peaks in the residual
// The advantage of finding peaks in the residual instead of the spectrum is that spectrum energy is usually
// concentrated in the low frequencies, resulting in a clustering of the highest peaks in the lows. Getting
// peaks from the residual distributes the peaks more evenly across the spectrum.
var peaks = peakHistory.List; // take oldest list,
peaks.Clear(); // clear it, and
FindLocalMaxima(spectrumResidual, peaks); // refill with new peaks
// Pick the largest n peaks
int numMaxima = Math.Min(peaks.Count, profile.PeaksPerFrame);
if (numMaxima > 0)
{
peaks.Sort((p1, p2) => p1.Value == p2.Value ? 0 : p1.Value < p2.Value ? 1 : -1); // order peaks by height
if (peaks.Count > numMaxima)
{
peaks.RemoveRange(numMaxima, peaks.Count - numMaxima); // select the n tallest peaks by deleting the rest
}
peaks.Sort((p1, p2) => p1.Index == p2.Index ? 0 : p1.Index < p2.Index ? -1 : 1); // sort peaks by index (not really necessary)
}
peakHistory.Add(index, peaks);
if (FrameProcessed != null)
{
// Mark peaks as 0dB for spectrogram display purposes
foreach (var peak in peaks)
{
spectrum[peak.Index] = 0;
spectrumResidual[peak.Index] = 0;
}
FrameProcessed(this, new FrameProcessedEventArgs {
AudioTrack = track, Index = index, Indices = indices,
Spectrum = spectrum, SpectrumResidual = spectrumResidual
});
}
processedFrames++;
index++;
if (processedFrames >= peakHistory.Length)
{
peakPairs.Clear();
FindPairsWithMaxEnergy(peakHistory, peakPairs);
ConvertPairsToSubFingerprints(peakPairs, subFingerprints);
}
if (subFingerprints.Count > 512)
{
FireFingerprintHashesGenerated(track, indices, subFingerprints);
subFingerprints.Clear();
}
}
// Flush the remaining peaks of the last frames from the history to get all remaining pairs
for (int i = 0; i < profile.TargetZoneLength; i++)
{
var peaks = peakHistory.List;
peaks.Clear();
peakHistory.Add(-1, peaks);
peakPairs.Clear();
FindPairsWithMaxEnergy(peakHistory, peakPairs);
ConvertPairsToSubFingerprints(peakPairs, subFingerprints);
}
FireFingerprintHashesGenerated(track, indices, subFingerprints);
audioStream.Close();
}