// #############################################################################################################################
// ################################# FOUR DIFFERENT METHODS TO CALCULATE THE BACKGROUND NOISE PROFILE
//
// (1) MODAL METHOD
// (2) LOWEST PERCENTILE FRAMES METHOD
// (3) BIN-WISE LOWEST PERCENTILE CELLS METHOD
// (4) FIRST N FRAMES
// ##################
/// <summary>
/// (1) MODAL METHOD
/// Assumes the passed matrix is a spectrogram. i.e. rows=frames, cols=freq bins.
/// Returns the noise profile over freq bins. i.e. one noise value per freq bin.
/// </summary>
/// <param name="matrix">the spectrogram with origin top-left</param>
/// <param name="sdCount">number of standard deviations</param>
public static NoiseProfile CalculateModalNoiseProfile(double[,] matrix, double sdCount)
{
int colCount = matrix.GetLength(1);
double[] noiseMode = new double[colCount];
double[] noiseSd = new double[colCount];
double[] noiseThreshold = new double[colCount];
double[] minsOfBins = new double[colCount];
double[] maxsOfBins = new double[colCount];
for (int col = 0; col < colCount; col++)
{
double[] freqBin = MatrixTools.GetColumn(matrix, col);
SNR.BackgroundNoise binNoise = SNR.CalculateModalBackgroundNoiseInSignal(freqBin, sdCount);
noiseMode[col] = binNoise.NoiseMode;
noiseSd[col] = binNoise.NoiseSd;
noiseThreshold[col] = binNoise.NoiseThreshold;
minsOfBins[col] = binNoise.MinDb;
maxsOfBins[col] = binNoise.MaxDb;
}
var profile = new NoiseProfile()
{
NoiseMode = noiseMode,
NoiseSd = noiseSd,
NoiseThresholds = noiseThreshold,
MinDb = minsOfBins,
MaxDb = maxsOfBins,
};
return(profile);
}
/// <summary>
/// This method normalizes a score array by subtracting the mode rather than the average of the array.
/// This is because the noise is often not normally distributed but rather skewed.
/// However, did not work well.
/// </summary>
public static List <Plot> SubtractModeAndSd(List <Plot> plots)
{
var opPlots = new List <Plot>();
// subtract average from each plot array
foreach (var plot in plots)
{
var scores = plot.data;
var bgn = SNR.CalculateModalBackgroundNoiseInSignal(scores, 1.0);
var mode = bgn.NoiseMode;
var sd = bgn.NoiseSd;
// normalize the scores to z-scores
for (int i = 0; i < scores.Length; i++)
{
// Convert scores to z-scores
scores[i] = (scores[i] - mode) / sd;
if (scores[i] < 0.0)
{
scores[i] = 0.0;
}
if (scores[i] > 4.0)
{
scores[i] = 4.0;
}
// normalize full scale to 4 SDs.
scores[i] /= 4.0;
}
opPlots.Add(plot);
}
return(opPlots);
}