public static int Results(float[] fftOutput, float[] result, float decibelOffset)
{
float max = float.MinValue;
int peak = -1;
int y = 0;
for (int x = 0; x < fftOutput.Length; x += 2)
{
// calculate magnitude of a FFT bin (L2 norm)
// divide magnitudes by FFT input length (so that they aren't dependent in the input length)
// multiply by 2 since the FFT result only contains half of the energy (the second half are the negative frequencies of the "full" FFT result)
// calculate dB scale value
// http://www.mathworks.de/support/tech-notes/1700/1702.html
result[y] = (float)VolumeUtil.LinearToDecibel(
CalculateMagnitude(fftOutput[x], fftOutput[x + 1]) / fftOutput.Length * 2) + decibelOffset;
if (result[y] > max)
{
max = result[y];
peak = y;
}
if (float.IsNegativeInfinity(result[y]))
{
result[y] = float.MinValue;
}
y++;
}
return(peak);
}
/// <summary>
/// Normalizes the vertical-axis scale of a FFT result and transforms it to a logarithmic dB
/// scale for a better visualization. The resulting dB values aren't absolute, but relative to the
/// main peak (highest peak).
/// See: Windowing Functions Improve FFT Results, Part II (http://www.tmworld.com/article/325630-Windowing_Functions_Improve_FFT_Results_Part_II.php)
/// </summary>
/// <param name="fftOutput">the output of a FFT function (interleaved complex numbers)</param>
/// <param name="normalizedResult">the normalized result for visualization</param>
public static void NormalizeResults(float[] fftOutput, float[] normalizedResult)
{
float max = float.MinValue;
int y = 0;
for (int x = 0; x < fftOutput.Length; x += 2)
{
// calculate magnitude of a FFT bin (L2 norm)
normalizedResult[y] = CalculateMagnitude(fftOutput[x], fftOutput[x + 1]);
// find out max value for normalization
if (normalizedResult[y] > max)
{
max = normalizedResult[y];
}
y++;
}
for (int x = 0; x < normalizedResult.Length; x++)
{
// normalize by max value & calculate dB scale value
normalizedResult[x] = (float)VolumeUtil.LinearToDecibel(normalizedResult[x] / max);
}
}
