C#开源的信号处理库NWaves

GitHub地址

https://github.com/ar1st0crat/NWaves

CSDN下载地址

https://download.csdn.net/download/g313105910/16588600

安装

可在NuGet上获得。通过

PM> Install-Package NWaves

阅读wiki文档

新版本0.9.4出局了！更快，更聪明，更有特色。在这里阅读有关更改的文章

DSP非专家注意事项

面向MATLAB/SciPy用户的NWaves

观看调查录像

主要特征

主要DSP变换(FFT、DCT、MDCT、STFT、FWT、Hilbert、Hartley、Mellin、倒谱、Goertzel)
信号建设者(正弦，白色/粉红色/红色/Perlin噪声，awgn，三角形，锯齿，方形，脉冲，斜坡，ADSR，可波)
基本LTI数字滤波器(移动平均，梳状，Savitzky-Golay，预/消重点，直流移除，Rasta)
FIR/IIR滤波(离线和在线)，零相位滤波
双四通滤波器(低通，高通，带通，缺口，全通，峰值，搁置)
1极滤波器(低通，高通)
IIR滤波器(Bessel，Butterworth，Chebyshev I&II，Elliptic，Thiran)
基本运算(卷积、互相关、整流、放大)
块卷积(重叠-添加/重叠-脱机和在线保存)
基本滤波器设计和分析(群延迟、零点/极点、BP、BR、HP从Lp到LP、SOS、组合滤波器)
LTI滤波器的状态空间表示
FIR滤波器设计：频率采样，窗口-正弦，等波纹(Remez/Parks-McClellan)
非线性滤波器(中值滤波、过驱动和失真效应)
窗口功能(Hamming，Blackman，Hann，高斯，Kaiser，KBD，三角形，Lanczos，平顶，Bartlett-Hann)
心理声学滤波器组(MEL，BARK，临界波段，ERB，八度)和VTLN翘曲
可定制的特征提取(时域、光谱、MFCC、PNCC/SPNCC、LPC、LPCC、PLP、AMS)
预配置的MFCC萃取器：HTK(MFCC-FB24)，Slaney(MFCC-FB40)
LPC转换：LPC<->倒谱，LPC<->LSF
特征后处理(均值和方差归一化，添加增量)和csv序列化
光谱特征(质心，扩展，平整度，熵，滚转，对比度，峰顶，降低，噪声，mpeg 7)
谐波特征(谐波质心和传播，不谐波，三刺激，奇偶比)
时域特性(均方根、能量、过零率、熵)
基音跟踪(自相关，INE，ZCR+Schmitt触发器，HSS/HPS，倒谱)
时间尺度修正(相位声码器，具有相同相位锁定的PV，WSOLA，PaulStretch)
简单重采样，插值，抽取
带限重采样
小波：Haar，db，symlet，coiflet
多相滤波器
降噪(谱减法，科幻式维纳滤波)
信封跟随
声音效果(回声，颤音，瓦华，相位，合唱，颤音，侧边音，音高移动，变形，机器人化，耳语)
谐波/撞击分离
Griffin-Lim算法
Karplus-强合成
PADSynth合成
自适应滤波(LMS，NLMS，LMF，SignLMS，RLS)
简单调制/解调(AM，环形，调频，PM)
简单的音频回放和录音

NWaves哲学

NWaves最初是为了研究、可视化和教授DSP和声音编程的基础知识。所有算法都是用C#编写的，尽可能简单，最初主要是为脱机处理而设计的(现在也有许多在线方法可用)。但这并不意味着库只能用于玩具项目；是的，它不是用C/C++或ASM编写的，但不是这样非常为了许多目的，也要慢慢来。

快速启动

与一维信号一起工作

// Create signal from samples repeated 100 timesfloat[] 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);

信号建设者

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();

信号建设者也可以充当实时样本生成器：

SignalBuilder lfo = new TriangleWaveBuilder().SetParameter("min", 100).SetParameter("max", 1500).SetParameter("frequency", 2.0/*Hz*/).SampledAt(16000/*Hz*/);//while (...)
{var sample = lfo.NextSample();//...
}

信号和波形文件：

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

变换

对于每个转换，都有一个相应的转换器对象。每个转换器对象Direct()和Inverse()方法。

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) -> realvar magnitudeSpectrum = rfft.MagnitudeSpectrum(signal[1000, 2024]);var powerSpectrum = rfft.PowerSpectrum(signal.First(1024), normalize: false);// ...
NWaves中的许多方法都重载了以输出缓冲区作为参数的版本。因此，只要有可能，就重用内存：float[] spectrum = new float[1024];for (var i = start; i < end; i += step)
{rfft.MagnitudeSpectrum(signal[i, i + 1024], spectrum);// ...// do something with spectrum
}
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);
倒谱变换// 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);
小波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);

业务：

// convolutionvar conv = Operation.Convolve(signal, kernel);
var xcorr = Operation.CrossCorrelate(signal1, signal2);// block convolutionvar filtered = Operation.BlockConvolve(signal, kernel, 4096, FilteringMethod.OverlapAdd);// resamplingvar 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 modificationvar stretch = Operation.TimeStretch(signal, 0.7, TsmAlgorithm.Wsola);
var cool = Operation.TimeStretch(signal, 16, TsmAlgorithm.PaulStretch);// envelope followingvar envelope = Operation.Envelope(signal);// rectificationvar halfRect = Operation.HalfRectify(signal);
var fullRect = Operation.FullRectify(signal);// spectral subtractionvar clean = Operation.SpectralSubtract(signal, noise);

过滤器和效果：

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 tutorialvar 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, 0.15);
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, 0.05, 0.15);// 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);

在线处理

对象的所有类都支持联机处理。IOnlineFilter接口。目前，所有的LTI过滤器，FilterChain类，块卷积器(OlaBlockConvolver, OlsBlockConvolver)和音频效果包含Process(sample)和Process(bufferIn, bufferOut)方法负责在线处理。

简单地处理一个接一个的数据样本：

var outputSample = filter.Process(sample);
或者准备必要的缓冲区(或者只是使用它们，如果它们来自您系统的另一部分)：float[] output;...void NewChunkAvailable(float[] chunk)
{filter.Process(chunk, output);
}// if input chunk shouldn't necessarily be preserved, it can be overwritten:void NewChunkAvailable(float[] chunk)
{filter.Process(chunk, chunk);
}
分块卷积器：// Overlap-Add / Overlap-SaveFirFilter 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);
}

特征提取器

高度可定制的特征提取器可用于离线和在线处理(MFCC系列、LPC、音调等)。

var mfccOptions = new MfccOptions
{SamplingRate = signal.SamplingRate,FeatureCount = 13,FrameDuration = 0.032/*sec*/,HopDuration = 0.015/*sec*/,FilterBankSize = 26,PreEmphasis = 0.97,//...unspecified parameters will have default values
};var mfccExtractor = new MfccExtractor(mfccOptions);
var mfccVectors = mfccExtractor.ComputeFrom(signal);// serialize current config to JSON file:using (var config = new FileStream("file.json", FileMode.Create))
{config.SaveOptions(mfccOptions);
}var lpcOptions = new LpcOptions
{SamplingRate = signal.SamplingRate,LpcOrder = 15
};var lpcExtractor = new LpcExtractor(lpcOptions);
var lpcVectors = lpcExtractor.ParallelComputeFrom(signal);var opts = new MultiFeatureOptions
{SamplingRate = signal.SamplingRate,FeatureList = "centroid, flatness, c1+c2+c3"
};var spectralExtractor = new SpectralFeaturesExtractor(opts);opts.FeatureList = "all";
var tdExtractor = new TimeDomainFeaturesExtractor(opts);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// open config from JSON file:PnccOptions options;
using (var config = new FileStream("file.json", FileMode.Open))
{options = config.LoadOptions<PnccOptions>();
}var pnccExtractor = new PnccExtractor(pnccOptions);
var pnccVectors = pnccExtractor.ComputeFrom(signal, /*from*/1000, /*to*/60000 /*sample*/);
FeaturePostProcessing.NormalizeMean(pnccVectors);// serializationusing (var csvFile = new FileStream("mfccs.csv", FileMode.Create))
{var serializer = new CsvFeatureSerializer(mfccVectors);await serializer.SerializeAsync(csvFile);
}

前处理

在语音处理中，在信号的主处理之前，通常会用到预强调滤波器.

有三个选项可以执行预强调过滤：

特征提取器构造函数中预强调系数的设置
在处理和处理滤波信号之前应用滤波器。
对信号进行就地滤波和处理。

第一个选项稍微慢一些，但是它不会分配额外的内存，也不会改变输入信号(因此，在默认情况下，它可能应该是选择)。如果不需要保留输入信号，那么第三种选择是最好的。如果输入信号必须被保留，并且没有额外的内存问题，那么第二种方法是首选的(它会更快)。

// option 1:var opts = new MfccOptions
{SamplingRate = signal.SamplingRate,FeatureCount = 13,PreEmphasis = 0.95
};
var mfccExtractor = new MfccExtractor(opts);
var mfccVectors = mfccExtractor.ComputeFrom(signal);// option 2:
// ApplyTo() will create new signal (allocate new memory)opts.PreEmphasis = 0;
mfccExtractor = new MfccExtractor(opts);
var pre = new PreEmphasisFilter(0.95);
var filtered = pre.ApplyTo(signal);
mfccVectors = mfccExtractor.ComputeFrom(filtered);// option 3:
// process array or DiscreteSignal samples in-place:for (var i = 0; i < signal.Length; i++)
{signal[i] = pre.Process(signal[i]);
}
// or simply:
// pre.Process(signal.Samples, signal.Samples);mfccVectors = mfccExtractor.ComputeFrom(signal);

播放录音

MciAudioPlayer和MciAudioRecorder只在Windows端工作，因为它们使用winmm.dll和mci命令。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();// recordingIAudioRecorder recorder = new MciAudioRecorder();// ...in some event handler
recorder.StartRecording(16000);// ...in some event handler
recorder.StopRecording("temp.wav");