public void TestFilterAppliedDirectly()
{
AssertFilterOutput(_filter.ApplyTo(_signal, FilteringMethod.DifferenceEquation));
}
/// <summary>
/// Method creates overlapping ERB filters (ported from Malcolm Slaney's MATLAB code).
/// </summary>
/// <param name="erbFilterCount">Number of ERB filters</param>
/// <param name="fftSize">Assumed size of FFT</param>
/// <param name="samplingRate">Assumed sampling rate</param>
/// <param name="lowFreq">Lower bound of the frequency range</param>
/// <param name="highFreq">Upper bound of the frequency range</param>
/// <param name="normalizeGain">True if gain should be normalized; false if all filters should have same height 1.0</param>
/// <returns>Array of ERB filters</returns>
public static float[][] Erb(
int erbFilterCount, int fftSize, int samplingRate, double lowFreq = 0, double highFreq = 0, bool normalizeGain = true)
{
if (lowFreq < 0)
{
lowFreq = 0;
}
if (highFreq <= lowFreq)
{
highFreq = samplingRate / 2.0;
}
const double earQ = 9.26449;
const double minBw = 24.7;
const double bw = earQ * minBw;
const int order = 1;
var t = 1.0 / samplingRate;
var frequencies = new double[erbFilterCount];
for (var i = 1; i <= erbFilterCount; i++)
{
frequencies[erbFilterCount - i] =
-bw + Math.Exp(i * (-Math.Log(highFreq + bw) + Math.Log(lowFreq + bw)) / erbFilterCount) * (highFreq + bw);
}
var ucirc = new Complex[fftSize / 2 + 1];
for (var i = 0; i < ucirc.Length; i++)
{
ucirc[i] = Complex.Exp((2 * Complex.ImaginaryOne * i * Math.PI) / fftSize);
}
var rootPos = Math.Sqrt(3 + Math.Pow(2, 1.5));
var rootNeg = Math.Sqrt(3 - Math.Pow(2, 1.5));
var fft = new Fft(fftSize);
var erbFilterBank = new float[erbFilterCount][];
for (var i = 0; i < erbFilterCount; i++)
{
var cf = frequencies[i];
var erb = Math.Pow(Math.Pow(cf / earQ, order) + Math.Pow(minBw, order), 1.0 / order);
var b = 1.019 * 2 * Math.PI * erb;
var theta = 2 * cf * Math.PI * t;
var itheta = Complex.Exp(2 * Complex.ImaginaryOne * theta);
var a0 = t;
var a2 = 0.0;
var b0 = 1.0;
var b1 = -2 * Math.Cos(theta) / Math.Exp(b * t);
var b2 = Math.Exp(-2 * b * t);
var common = -t *Math.Exp(-b *t);
var k1 = Math.Cos(theta) + rootPos * Math.Sin(theta);
var k2 = Math.Cos(theta) - rootPos * Math.Sin(theta);
var k3 = Math.Cos(theta) + rootNeg * Math.Sin(theta);
var k4 = Math.Cos(theta) - rootNeg * Math.Sin(theta);
var a11 = common * k1;
var a12 = common * k2;
var a13 = common * k3;
var a14 = common * k4;
var gainArg = Complex.Exp(Complex.ImaginaryOne * theta - b * t);
var gain = Complex.Abs(
(itheta - gainArg * k1) *
(itheta - gainArg * k2) *
(itheta - gainArg * k3) *
(itheta - gainArg * k4) *
Complex.Pow(t * Math.Exp(b * t) / (-1.0 / Math.Exp(b * t) + 1 + itheta * (1 - Math.Exp(b * t))), 4.0));
var filter1 = new IirFilter(new[] { a0, a11, a2 }, new[] { b0, b1, b2 });
var filter2 = new IirFilter(new[] { a0, a12, a2 }, new[] { b0, b1, b2 });
var filter3 = new IirFilter(new[] { a0, a13, a2 }, new[] { b0, b1, b2 });
var filter4 = new IirFilter(new[] { a0, a14, a2 }, new[] { b0, b1, b2 });
var ir = new double[fftSize];
ir[0] = 1.0;
// for doubles the following code will work ok
// (however there's a crucial lost of precision in case of floats):
//var filter = filter1 * filter2 * filter3 * filter4;
//ir = filter.ApplyTo(ir);
// this code is ok both for floats and for doubles:
ir = filter1.ApplyTo(ir);
ir = filter2.ApplyTo(ir);
ir = filter3.ApplyTo(ir);
ir = filter4.ApplyTo(ir);
var kernel = new DiscreteSignal(1, ir.Select(s => (float)(s / gain)));
erbFilterBank[i] = fft.PowerSpectrum(kernel, false).Samples;
}
// normalize gain (by default)
if (!normalizeGain)
{
return(erbFilterBank);
}
foreach (var filter in erbFilterBank)
{
var sum = 0.0;
for (var j = 0; j < filter.Length; j++)
{
sum += Math.Abs(filter[j] * filter[j]);
}
var weight = Math.Sqrt(sum * samplingRate / fftSize);
for (var j = 0; j < filter.Length; j++)
{
filter[j] = (float)(filter[j] / weight);
}
}
return(erbFilterBank);
}
public void OfflineFilterForLoop()
{
var output = _filter.ApplyTo(_signal);
}
public void Run()
{
var output2 = new float[_signal.Length];
var output4 = new float[_signal.Length];
var output5 = new float[_signal.Length];
var outputZi = new float[_signal.Length];
var samples = _signal.Samples;
for (var i = 0; i < samples.Length; i++)
{
output4[i] = _filterV4BiQuad.Process(samples[i]);
output5[i] = _filterV5BiQuad.Process(samples[i]);
outputZi[i] = _filterZiBiQuad.Process(samples[i]);
}
var diffAverageV4 = output5.Zip(output4, (o5, o4) => Math.Abs(o5 - o4)).Average();
var diffAverageZi = output5.Zip(outputZi, (o5, zi) => Math.Abs(o5 - zi)).Average();
Console.WriteLine($"Average difference Ver.0.9.5 vs. Ver.0.9.4 : {diffAverageV4}");
Console.WriteLine($"Average difference IirFilter vs. ZiFilter : {diffAverageZi}");
for (var i = 0; i < samples.Length; i++)
{
output4[i] = _filterV4Butterworth6.Process(samples[i]);
output5[i] = _filterV5Butterworth6.Process(samples[i]);
outputZi[i] = _filterZiButterworth6.Process(samples[i]);
}
diffAverageV4 = output5.Zip(output4, (o5, o4) => Math.Abs(o5 - o4)).Average();
diffAverageZi = output5.Zip(outputZi, (o5, zi) => Math.Abs(o5 - zi)).Average();
Console.WriteLine($"Average difference Ver.0.9.5 vs. Ver.0.9.4 : {diffAverageV4}");
Console.WriteLine($"Average difference IirFilter vs. ZiFilter : {diffAverageZi}");
// === MISC ====
var med = new MedianFilter();
var med2 = new MedianFilter2();
var medOut = med.ApplyTo(_signal).Samples;
var medOut2 = med2.ApplyTo(_signal).Samples;
var diffAverageMed = medOut.Zip(medOut, (m1, m2) => Math.Abs(m1 - m2)).Average();
Console.WriteLine($"Average difference MedianFilter vs. MedianFilter2 : {diffAverageMed}");
var ma = new MovingAverageFilter();
var maRec = new MovingAverageRecursiveFilter();
var maOut = ma.ApplyTo(_signal).Samples;
var maRecOut = maRec.ApplyTo(_signal).Samples;
var diffAverageMa = maOut.Zip(maRecOut, (m1, m2) => Math.Abs(m1 - m2)).Average();
Console.WriteLine($"Average difference MovingAverageFilter vs. MovingAverageRecursiveFilter : {diffAverageMa}");
// 32bit vs. 64bit
var fir32 = new FirFilter(DesignFilter.FirWinLp(7, 0.1));
var fir64 = new FirFilter64(DesignFilter.FirWinLp(7, 0.1));
var fir32Out = fir32.ApplyTo(_signal).Samples;
var fir64Out = fir64.ApplyTo(_signal.Samples.ToDoubles());
var diffAverageFir = fir64Out.Zip(fir32Out, (m1, m2) => Math.Abs(m1 - m2)).Average();
Console.WriteLine($"Average difference FirFilter vs. FirFilter64 : {diffAverageFir}");
var iir32 = new IirFilter(_filterV5Butterworth6.Tf);
var iir64 = new IirFilter64(_filterV5Butterworth6.Tf);
var iir32Out = iir32.ApplyTo(_signal).Samples;
var iir64Out = iir64.ApplyTo(_signal.Samples.ToDoubles());
var diffAverageIir = iir64Out.Zip(iir32Out, (m1, m2) => Math.Abs(m1 - m2)).Average();
Console.WriteLine($"Average difference IirFilter vs. IirFilter64 : {diffAverageIir}");
var zi32 = new ZiFilter(_filterV5Butterworth6.Tf);
var zi64 = new ZiFilter64(_filterV5Butterworth6.Tf);
var zi32Out = zi32.ApplyTo(_signal).Samples;
var zi64Out = zi64.ApplyTo(_signal.Samples.ToDoubles());
var diffAverageZis = zi64Out.Zip(zi32Out, (m1, m2) => Math.Abs(m1 - m2)).Average();
Console.WriteLine($"Average difference ZiFilter vs. ZiFilter64 : {diffAverageZis}");
zi32Out = zi32.ZeroPhase(_signal).Samples;
zi64Out = zi64.ZeroPhase(_signal.Samples.ToDoubles());
var diffAverageZiZeroPhase = zi64Out.Zip(zi32Out, (m1, m2) => Math.Abs(m1 - m2)).Average();
Console.WriteLine($"Average difference ZiFilter vs. ZiFilter64 (zero-phase): {diffAverageZiZeroPhase}");
}
