// These work by shifting the signal until it seems to correlate with itself.
// In other words if the signal looks very similar to (signal shifted 200 samples) than the fundamental period is probably 200 samples
// Note that the algorithm only works well when there's only one prominent fundamental.
// This could be optimized by looking at the rate of change to determine a maximum without testing all periods.
private static double[] detectPitchCalculation(WaveAudio w, double minHz, double maxHz, int nCandidates, int nResolution, PitchDetectAlgorithm algorithm)
{
// note that higher frequency means lower period
int nLowPeriodInSamples = hzToPeriodInSamples(maxHz, w.getSampleRate());
int nHiPeriodInSamples = hzToPeriodInSamples(minHz, w.getSampleRate());
if (nHiPeriodInSamples <= nLowPeriodInSamples) throw new Exception("Bad range for pitch detection.");
if (w.getNumChannels() != 1) throw new Exception("Only mono supported.");
double[] samples = w.data[0];
if (samples.Length < nHiPeriodInSamples) throw new Exception("Not enough samples.");
// both algorithms work in a similar way
// they yield an array of data, and then we find the index at which the value is highest.
double[] results = new double[nHiPeriodInSamples - nLowPeriodInSamples];
if (algorithm == PitchDetectAlgorithm.Amdf)
{
for (int period = nLowPeriodInSamples; period < nHiPeriodInSamples; period += nResolution)
{
double sum = 0;
// for each sample, see how close it is to a sample n away. Then sum these.
for (int i = 0; i < samples.Length - period; i++)
sum += Math.Abs(samples[i] - samples[i + period]);
double mean = sum / (double)samples.Length;
mean *= -1; //somewhat of a hack. We are trying to find the minimum value, but our findBestCandidates finds the max. value.
results[period - nLowPeriodInSamples] = mean;
}
}
else if (algorithm == PitchDetectAlgorithm.Autocorrelation)
{
for (int period = nLowPeriodInSamples; period < nHiPeriodInSamples; period += nResolution)
{
double sum = 0;
// for each sample, find correlation. (If they are far apart, small)
for (int i = 0; i < samples.Length - period; i++)
sum += samples[i] * samples[i + period];
double mean = sum / (double)samples.Length;
results[period - nLowPeriodInSamples] = mean;
}
}
// find the best indices
int[] bestIndices = findBestCandidates(nCandidates, ref results); //note findBestCandidates modifies parameter
// convert back to Hz
double[] res = new double[nCandidates];
for (int i=0; i<nCandidates;i++)
res[i] = periodInSamplesToHz(bestIndices[i]+nLowPeriodInSamples, w.getSampleRate());
return res;
}
/// <param name="nCandidates">Number of results to return</param>
public static double[] DetectPitchCandidates(WaveAudio w, double minHz, double maxHz, int nCandidates, PitchDetectAlgorithm algorithm)
{
return detectPitchCalculation(w, minHz, maxHz, nCandidates, 1, algorithm);
}
/// <param name="nCandidates">Number of results to return</param>
/// <param name="nResolution">Resolution. A value of 1, default, is slowest but most precise.</param>
public static double[] DetectPitchCandidates(WaveAudio w, double minHz, double maxHz, int nCandidates, PitchDetectAlgorithm algorithm, int nResolution)
{
if (nResolution < 1) throw new Exception("Resultion must be >= 1");
return detectPitchCalculation(w, minHz, maxHz, nCandidates, nResolution, algorithm);
}
public static double DetectPitch(WaveAudio w, double minHz, double maxHz, PitchDetectAlgorithm algorithm)
{
return detectPitchCalculation(w, minHz, maxHz, 1, 1, algorithm)[0];
}
// These work by shifting the signal until it seems to correlate with itself.
// In other words if the signal looks very similar to (signal shifted 200 samples) than the fundamental period is probably 200 samples
// Note that the algorithm only works well when there's only one prominent fundamental.
// This could be optimized by looking at the rate of change to determine a maximum without testing all periods.
private static double[] detectPitchCalculation(WaveAudio w, double minHz, double maxHz, int nCandidates, int nResolution, PitchDetectAlgorithm algorithm)
{
// note that higher frequency means lower period
int nLowPeriodInSamples = hzToPeriodInSamples(maxHz, w.getSampleRate());
int nHiPeriodInSamples = hzToPeriodInSamples(minHz, w.getSampleRate());
if (nHiPeriodInSamples <= nLowPeriodInSamples)
{
throw new Exception("Bad range for pitch detection.");
}
if (w.getNumChannels() != 1)
{
throw new Exception("Only mono supported.");
}
double[] samples = w.data[0];
if (samples.Length < nHiPeriodInSamples)
{
throw new Exception("Not enough samples.");
}
// both algorithms work in a similar way
// they yield an array of data, and then we find the index at which the value is highest.
double[] results = new double[nHiPeriodInSamples - nLowPeriodInSamples];
if (algorithm == PitchDetectAlgorithm.Amdf)
{
for (int period = nLowPeriodInSamples; period < nHiPeriodInSamples; period += nResolution)
{
double sum = 0;
// for each sample, see how close it is to a sample n away. Then sum these.
for (int i = 0; i < samples.Length - period; i++)
{
sum += Math.Abs(samples[i] - samples[i + period]);
}
double mean = sum / (double)samples.Length;
mean *= -1; //somewhat of a hack. We are trying to find the minimum value, but our findBestCandidates finds the max. value.
results[period - nLowPeriodInSamples] = mean;
}
}
else if (algorithm == PitchDetectAlgorithm.Autocorrelation)
{
for (int period = nLowPeriodInSamples; period < nHiPeriodInSamples; period += nResolution)
{
double sum = 0;
// for each sample, find correlation. (If they are far apart, small)
for (int i = 0; i < samples.Length - period; i++)
{
sum += samples[i] * samples[i + period];
}
double mean = sum / (double)samples.Length;
results[period - nLowPeriodInSamples] = mean;
}
}
// find the best indices
int[] bestIndices = findBestCandidates(nCandidates, ref results); //note findBestCandidates modifies parameter
// convert back to Hz
double[] res = new double[nCandidates];
for (int i = 0; i < nCandidates; i++)
{
res[i] = periodInSamplesToHz(bestIndices[i] + nLowPeriodInSamples, w.getSampleRate());
}
return(res);
}
/// <param name="nCandidates">Number of results to return</param>
/// <param name="nResolution">Resolution. A value of 1, default, is slowest but most precise.</param>
public static double[] DetectPitchCandidates(WaveAudio w, double minHz, double maxHz, int nCandidates, PitchDetectAlgorithm algorithm, int nResolution)
{
if (nResolution < 1)
{
throw new Exception("Resultion must be >= 1");
}
return(detectPitchCalculation(w, minHz, maxHz, nCandidates, nResolution, algorithm));
}
/// <param name="nCandidates">Number of results to return</param>
public static double[] DetectPitchCandidates(WaveAudio w, double minHz, double maxHz, int nCandidates, PitchDetectAlgorithm algorithm)
{
return(detectPitchCalculation(w, minHz, maxHz, nCandidates, 1, algorithm));
}
public static double DetectPitch(WaveAudio w, double minHz, double maxHz, PitchDetectAlgorithm algorithm)
{
return(detectPitchCalculation(w, minHz, maxHz, 1, 1, algorithm)[0]);
}
