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(); }