Known bugs and changelog - ar1st0crat/NWaves GitHub Wiki

var mfccOptions = new MfccOptions
{
    SamplingRate = 22050,
    FeatureCount = 13,
    FrameDuration = 0.032/*sec*/,
    HopDuration = 0.015/*sec*/,
    FilterBankSize = 26,
    HighFrequency = 6000,
    PreEmphasis = 0.97,
    //...unspecified parameters will have default values 
};

var mfccExtractor = new MfccExtractor(mfccOptions);
List<float[]> mfccVectors = mfccExtractor.ComputeFrom(signal);

// { 0, 0.015, 0.030, 0.045, 0.060, 0.075, ... }
List<double> timeMarkers = mfccExtractor.TimeMarkers(mfccVectors.Count);

// serialize current config to JSON file:
using (var config = new FileStream("file.json", FileMode.Create))
{
    config.SaveOptions(mfccOptions);
}

// open config from JSON file:
PlpOptions options;
using (var config = new FileStream("file.json", FileMode.Open))
{
    // identical fields will be copied, other fields will be ignored
    options = config.LoadOptions<PlpOptions>();
}

// cast (identical fields will be copied, other fields will be ignored)
options = mfccOptions.Cast<MfccOptions, PlpOptions>();

// get all errors after validation:
List<string> errors = options.Errors;

// code in versions before 0.9.2
var filter = DesignFilter.FirLp(145, 0.15f);
var impulseResponse = filter.ImpulseResponse();
var magnitudeResponse = filter.FrequencyResponse().Magnitude;
var tf = filter.Tf;

// became:
var filter = new FirFilter(DesignFilter.FirWinLp(145, 0.15));
var impulseResponse = filter.Tf.ImpulseResponse();
var magnitudeResponse = filter.Tf.FrequencyResponse().Magnitude;
var tf = filter.Tf;  // looks the same, but now it's more optimized

// in short:
// FirFilter / IirFilter classes are now used only for filtering.
// Everything related to filter design & analysis as of ver.0.9.2
// is concentrated in TransferFunction class. It was here before,
// but FDA responsibilities were somewhat mixed between filters and Tf.

// Filter objects contained arrays of both 32-bit and 64-bit coefficients
// although filtering was (and still is) done with 32-bit floats
// and FDA was (and still is) done with 64-bit doubles.

// As of ver.0.9.2 filters contain only 32-bit coefficients for filtering
// and the reference to TransferFunction object (not null - only if needed).
// TF objects contain 64-bit numerator / denominator coefficients.

var filter = new IirFilter(new[] { 1, 0.2, -0.3 }, new[] { 1, 0.9 });
var tf = filter.Tf;
// in this case TransferFunction will be generated on the fly
// from 32-bit floats (so we'll lose precision).
// Internal Tf object of the filter is still null.
// Memory efficient, but this filter maybe lacks precision for filter analysis.
// If we need just to filter data, this is the best solution.

var filter = new IirFilter(new TransferFunction(new[]{ 1, 0.2, -0.3 }, new[]{ 1, 0.9 }));
var tf = filter.Tf;
// here the filter stores the reference to TF with 64-bit precision
// and Tf property returns this reference.
// If we use this filter only for filtering, then this constructor is redundant
// and memory inefficient.


// Surely, we can work just with TF object, without any filter objects:

var tf = new TransferFunction(new[] { 1, 0.2, -0.3 }, new[] { 1, 0.9 });
var zeros = tf.Zeros;
var freqResponse = tf.FrequencyResponse(1024);
// etc.

// 64-bit precision:
var tf1 = new TransferFunction(b, a);
var tf2 = new TransferFunction(d, c);
var tf = tf1 * tf2;

// 32-bit precision:
var filter1 = new IirFilter(b, a);
var filter2 = new IirFilter(d, c);
var filter = filter1 * filter2;


// also:
var hpFilter = DesignFilter.LpToHp(filter);
var lpFilter = DesignFilter.HpToLp(hpFilter);
var bpFilter = DesignFilter.FirBp(123, 0.05f, 0.15f);
var brFilter = DesignFilter.FirBr(201, 0.08f, 0.23f, WindowTypes.Kaiser);

// became:
var hpFilter = DesignFilter.FirLpToHp(filter);
var lpFilter = DesignFilter.FirHpToLp(hpFilter);
var bpFilter = DesignFilter.FirWinBp(123, 0.05, 0.15);
var brFilter = DesignFilter.FirWinBr(201, 0.08, 0.23, WindowTypes.Kaiser);