NWaves

NWaves is a .NET library for 1D signal processing focused specifically on audio processing.

Releases

NWaves is available on NuGet. Install it via

PM> Install-Package NWaves

New version 0.9.2 is out! Faster, smarter, more features. Read about changes here

Main features

Philosophy of NWaves

NWaves was initially intended for research, visualizing and teaching basics of DSP and sound programming. All algorithms are coded in C# as simple as possible and were first designed mostly for offline processing (now many online methods are also available). It doesn't mean, though, that the library could be used only in toy projects; yes, it's not written in C/C++ or Asm, but it's not that very slow for many purposes either.

Quickstart

Working with 1D signals

// Create signal from samples repeated 100 times

float[] samples = new [] { 0.5f, 0.2f, -0.3f, 1.2f, 1.6f, -1.8f, 0.3f, -0.2f };

var s = new DiscreteSignal(8000, samples).Repeat(100);

var length = s.Length;
var duration = s.Duration;

var echoSignal = s + s.Delay(50);

var marginSignal = s.First(64).Concatenate(s.Last(64));

var repeatMiddle = s[400, 500].Repeat(10);

var mean = s.Samples.Average();
var sigma = s.Samples.Average(x => (x - mean) * (x - mean));

var normSignal = s - mean;
normSignal.Attenuate(sigma);

Signal builders

DiscreteSignal sinusoid = 
    new SineBuilder()
        .SetParameter("frequency", 500.0/*Hz*/)
        .SetParameter("phase", Math.PI / 6)
        .OfLength(1000)
        .SampledAt(44100/*Hz*/)
        .Build();

DiscreteSignal noise = 
    new RedNoiseBuilder()
        .SetParameter("min", -2.5)
        .SetParameter("max", 2.5)
        .OfLength(800)
        .SampledAt(44100)
        .DelayedBy(200)
        .Build();

DiscreteSignal noisy = 
    new SineBuilder()
        .SetParameter("min", -10.0)
        .SetParameter("max", 10.0)
        .SetParameter("freq", 1200.0/*Hz*/)
        .OfLength(1000)
        .SampledAt(44100)
        .SuperimposedWith(noise)
        .Build();

Signal builders can also act as real-time generators of samples:

SignalBuilder lfo = 
    new TriangleWaveBuilder()
            .SetParameter("min", 100)
            .SetParameter("max", 1500)
            .SetParameter("frequency", 2.0/*Hz*/)
            .SampledAt(16000/*Hz*/);

//while (...)
{
    var sample = lfo.NextSample();
    //...
}

Signals and wave files:

DiscreteSignal left, right;

// load

using (var stream = new FileStream("sample.wav", FileMode.Open))
{
    var waveFile = new WaveFile(stream);
    left = waveFile[Channels.Left];
    right = waveFile[Channels.Right];
}

// save

using (var stream = new FileStream("saved_mono.wav", FileMode.Create))
{
    var waveFile = new WaveFile(left);
    waveFile.SaveTo(stream);
}

using (var stream = new FileStream("saved_stereo.wav", FileMode.Create))
{
    var waveFile = new WaveFile(new [] { left, right });
    waveFile.SaveTo(stream);
}

Transforms

For each transform there's a corresponding transformer object. Each transformer object has Direct() and Inverse() methods.

FFT

// Complex FFT transformer:

var fft = new Fft(1024);

// Real FFT transformer (faster):

var rfft = new RealFft(1024);


float[] real = signal.First(1024).Samples;
float[] imag = new float [1024];

// in-place FFT
fft.Direct(real, imag);

// ...do something with real and imaginary parts of the spectrum...

// in-place IFFT
fft.Inverse(real, imag);

// post-processed FFT:

var magnitudeSpectrum = 
    fft.MagnitudeSpectrum(signal[1000, 2024]);

var powerSpectrum = 
    fft.PowerSpectrum(signal.First(1024), normalize: false);

var logPowerSpectrum = 
    fft.PowerSpectrum(signal.Last(1024))
       .Samples
       .Select(s => Scale.ToDecibel(s))
       .ToArray();


// rfft will run faster;
// real FFT transforms real-valued signal to complex-valued spectrum,
// hence prototypes of methods are same, except Direct/Inverse:

rfft.Direct(real, real, imag);   // real -> (real, imag)
rfft.Inverse(real, imag, real);  // (real, imag) -> real

var magnitudeSpectrum = 
    rfft.MagnitudeSpectrum(signal[1000, 2024]);

var powerSpectrum = 
    rfft.PowerSpectrum(signal.First(1024), normalize: false);

// ...

STFT

// Short-Time Fourier Transform:

var stft = new Stft(1024, 256, WindowTypes.Hamming);
var timefreq = stft.Direct(signal);
var reconstructed = stft.Inverse(timefreq);

var spectrogram = stft.Spectrogram(signal);

Cepstral transform

// Cepstral transformer:

var ct = new CepstralTransform(24, fftSize: 512);

// complex cepstrum
var cepstrum = ct.Direct(signal);
// or
ct.Direct(input, output);

// real cepstrum
ct.RealCepstrum(input, output);

Wavelets

var fwt = new Fwt(192, new Wavelet("db5"));

// or
//var fwt = new Fwt(192, new Wavelet(WaveletFamily.Daubechies, 5));

var output = new float[192];
var reconstructed = new float[192];

fwt.Direct(input, output);
fwt.Inverse(output, reconstructed);

Operations:

// convolution

var conv = Operation.Convolve(signal, kernel);
var xcorr = Operation.CrossCorrelate(signal1, signal2);

// block convolution

var filtered = Operation.BlockConvolve(signal, kernel, 4096, FilteringMethod.OverlapAdd);

// resampling

var resampled = Operation.Resample(signal, 22050);
var interpolated = Operation.Interpolate(signal, 3);
var decimated = Operation.Decimate(signal, 2);
var updown = Operation.ResampleUpDown(signal, 3, 2);

// time scale modification

var stretch = Operation.TimeStretch(signal, 0.7, TsmAlgorithm.Wsola);
var cool = Operation.TimeStretch(signal, 16, TsmAlgorithm.PaulStretch);

// envelope following

var envelope = Operation.Envelope(signal);

// rectification

var halfRect = Operation.HalfRectify(signal);
var fullRect = Operation.FullRectify(signal);

// spectral subtraction

var clean = Operation.SpectralSubtract(signal, noise);

Filters and effects:

var maFilter = new MovingAverageFilter(7);
var smoothedSignal = maFilter.ApplyTo(signal);

var frequency = 800.0/*Hz*/;
var notchFilter = new BiQuad.NotchFilter(frequency / signal.SamplingRate);
var notchedSignal = notchFilter.ApplyTo(signal);


// filter analysis:

var transferFunction = new TransferFunction(new [] { 1, 0.5, 0.2 }, new [] { 1, -0.8, 0.3 });

var filter = new IirFilter(transferFunction);

// we can also write this:

// var filter = new IirFilter(new [] { 1, 0.5, 0.2 }, new [] { 1, -0.8, 0.3 });
// var transferFunction = filter.Tf;
// ...

// if we simply want to apply filter and don't care much about FDA precision:
// read more in tutorial


var impulseResponse = transferFunction.ImpulseResponse();
var magnitudeResponse = transferFunction.FrequencyResponse().Magnitude;
var phaseResponse = transferFunction.FrequencyResponse().Phase;

var b = transferFunction.Numerator;
var a = transferFunction.Denominator;
var zeros = transferFunction.Zeros;
var poles = transferFunction.Poles;

var gd = transferFunction.GroupDelay();
var pd = transferFunction.PhaseDelay();


// some examples of FIR filter design:

var kernel = DesignFilter.FirWinLp(345, 0.15f);
var lpFilter = new FirFilter(kernel);

// HP filter can be obtained from LP with the same cutoff frequency:
var hpFilter = DesignFilter.FirLpToHp(lpFilter);

// design BP filter
var bpFilter = DesignFilter.FirWinBp(123, 0.05f, 0.15f);

// design equiripple HP filter
var bpFilter = DesignFilter.FirEquirippleHp(123, 0.34, 0.355, 0.05, 0.95);


// sequence of filters:

var cascade = filter * firFilter * notchFilter;
var filtered = cascade.ApplyTo(signal);

// equivalent to:

var filtered = filter.ApplyTo(signal);
filtered = firFilter.ApplyTo(filtered);
filtered = notchFilter.ApplyTo(filtered);


// parallel combination of filters:

var parallel = filter1 + filter2;
filtered = parallel.ApplyTo(signal);


// audio effects:

var flanger = new FlangerEffect(signal.SamplingRate);
var wahwah = new WahwahEffect(signal.SamplingRate, lfoFrequency: 2/*Hz*/);

var processed = wahwah.ApplyTo(flanger.ApplyTo(signal));
// this will create intermediate copy of the signal


// FilterChain is memory-efficient:

var filters = new FilterChain();
filters.Add(flanger);
filters.Add(wahwah);

processed = filters.ApplyTo(signal);

Online processing

Online processing is supported by all classes that implement the IOnlineFilter interface. Currently, all LTI filters, FilterChain class, block convolvers (OlaBlockConvolver, OlsBlockConvolver) and audio effects contain the Process(sample) and Process(bufferIn, bufferOut) methods responsible for online processing.

Simply process data sample after sample:

var outputSample = filter.Process(sample);

Or prepare necessary buffers (or just use them if they come from another part of your system):

float[] output;

...

void NewChunkAvailable(float[] chunk)
{
    filter.Process(chunk, output);
}

Block convolvers:

// Overlap-Add / Overlap-Save

FirFilter filter = new FirFilter(kernel);

var blockConvolver = OlaBlockConvolver.FromFilter(filter, 4096);

// processing loop:
// while new input sample is available
{
    var outputSample = blockConvolver.Process(sample);
}

// or:
// while new input buffer is available
{
    blockConvolver.Process(input, output);
}

Feature extractors

Highly customizable feature extractors are available for offline and online processing (MFCC family, LPC, pitch and lot of others).

var sr = signal.SamplingRate;

var lpcExtractor = new LpcExtractor(sr, 16, 0.032/*sec*/, 0.015/*sec*/);
var lpcVectors = lpcExtractor.ComputeFrom(signal);


var mfccExtractor = new MfccExtractor(sr, 13, filterbankSize: 24, preEmphasis: 0.95);
var mfccVectors = mfccExtractor.ParallelComputeFrom(signal);


var tdExtractor = new TimeDomainFeaturesExtractor(sr, "all", frameDuration, hopDuration);
var spectralExtractor = new SpectralFeaturesExtractor(sr, "centroid, flatness, c1+c2+c3", 0.032, 0.015);

var vectors = FeaturePostProcessing.Join(
                tdExtractor.ParallelComputeFrom(signal), 
                spectralExtractor.ParallelComputeFrom(signal));

// each vector will contain 1) all time-domain features (energy, rms, entropy, zcr)
//                          2) specified spectral features


var pnccExtractor = new PnccExtractor(sr, 13);
var pnccVectors = pnccExtractor.ComputeFrom(signal, /*from*/1000, /*to*/10000 /*sample*/);
FeaturePostProcessing.NormalizeMean(pnccVectors);


// serialization

using (var csvFile = new FileStream("mfccs.csv", FileMode.Create))
{
    var serializer = new CsvFeatureSerializer(mfccVectors);
    await serializer.SerializeAsync(csvFile);
}

Pre-processing

// Extractors allow setting pre-emphasis coefficient.

// This is equivalent to applying pre-emphasis filter:

var mfccExtractor = new MfccExtractor(sr, 13, filterbankSize: 24);
var pre = new PreEmphasisFilter(0.95);

// option 1:
// ApplyTo() will create new signal (allocate new memory)
var mfccVectors = mfccExtractor.ParallelComputeFrom(pre.ApplyTo(signal));

// option 2:
// process array or DiscreteSignal samples in-place:

for (var i = 0; i < signal.Length; i++)
{
    signal[i] = pre.Process(signal[i]);
}

mfccVectors = mfccExtractor.ParallelComputeFrom(signal);

Playing and recording

MciAudioPlayer and MciAudioRecorder work only at Windows-side, since they use winmm.dll and MCI commands.

IAudioPlayer player = new MciAudioPlayer();

// play entire file
await player.PlayAsync("temp.wav");

// play file from 16000th sample to 32000th sample
await player.PlayAsync("temp.wav", 16000, 32000);


// ...in some event handler
player.Pause();

// ...in some event handler
player.Resume();

// ...in some event handler
player.Stop();


// recording

IAudioRecorder recorder = new MciAudioRecorder();

// ...in some event handler
recorder.StartRecording(16000);

// ...in some event handler
recorder.StopRecording("temp.wav");