DspFiltersNet 1.1.0

DspFiltersNet

DspFiltersNet is a digital signal processing library for .NET 8.0.

It allows the creation of the following FilterTypes:

1. MovingAverage
2. FrequencyFilters

For FrequencyFilters it allows the creation of the following filter design types:

1. Butterworth
2. Bessel
3. Chebyshev Type I
4. Chebyshev Type II
5. Elliptic

The package supports the creation of Lowpass, Highpass, Bandpass and Bandstop filters up to order 16.

The package only supports Filters and does not have other features. For more DSP features in C# you may want to check out <img src="https://raw.githubusercontent.com/ar1st0crat/NWaves/master/assets/logo/logo_draft.bmp" alt="NWaves" height="18"/> NWaves

Example Usage

using DspFiltersNet;

class Program
{
    static void Main()
    {
        // Define a butterworth lowpass of second order with a cutoff frequency of 10Hz and a sampling frequency of 1kHz.
        var filterData = new ButterworthFilterDefinition(FrequencyFilterType.LowPass, 10, 0, 2, 1000);
        // Create your filter instance
        var filterInstance = new FrequencyFilterInstance(filterData);
        // Use the filter instance to process your samples (single sample mode also available)
        var result = filterInstance.ProcessSamples([0.0000000000000000D, 0.5941312281110626D, 0.9637482198843191D, 0.9700934515686609D, 0.6131666751454520D]);
    }
}

Remarks

Internal function

The package first creates an analog lowpass prototype of the selected settings similar to the matlab functions besselap, butterap, cheb1ap and cheb2ap. After that the lowpass prototype is converted to the selected filter (Lowpass, Highpass, Bandpass, Bandstop). This all works on the Zeros, Poles and Gain notation. After that the filter is transformed to a digital filter using the bilinear transform. In the end the Zeros, Poles and Gain returned from the bilinear transform are converted to a transfer function which is then used to in the filter instance to filter user data.

Validation

The code is validated with UnitTests against testdata generated with Matlab and with SciPy and should match these implementations pretty much perfectly (1e-14).