These are just the tools that I have a fair bit of familiarity with. the least-squares method or the Parks-McClellan method, you could have a look at a high-level optimization library such as CVX (works with MATLAB and Julia ), or JuMP for Julia. discrete-fourier-transform fir-filters fft-analysis. There are a lot of others, but these are the ones that I know have high quality libraries/methods for filter design.įor Julia, you can use the Filters.jl package, for Octave the signal package, for Scilab the Signal Processing toolbox, and for SciPy you have scipy.signal.Ī lot of filter design is done using optimization, and if you want to customize the cost-function or make other tweaks to e.g. Design and use digital filters with multiple programming languages. The DSP toolbox and the signal processing toolbox probably cover all of the well-known methods for digital filter design, as already mentioned.Īlternatives to MATLAB (that are free) include: Julia, Octave, Scilab, and SciPy (Python with libraries for technical computing). MATLAB is probably the most used software, at least in the university sector. A linear phase FIR Hilbert transformer, which has an anti-symmetrical impulse response, can be designed with either an odd length (Type III symmetry) or an even length (Type IV symmetry). The cic_process function).There are lots and lots of software that can aid you in designing digital filters. elements in DSP implementations, are required in an amount linearly related with the length of the filter. (the complete source code of this example is available in the documentation of In this example, we simulate the decimation of a signal from the original sampling rate of 10 MHz to a sampling rate of 625 KHz ![]() ![]() The CIC mini-toolbox enables both CIC decimation and interpolation simulation. The global filter (CIC and compensation FIR) has the desired response. Show a periodical frequency response (aliasing every 400 Hz), which is normal. Working at 400 Hz, that is, after the decimation by 16 (CIC decimation ratio), With the mini-toolbox, this can be done with the following command: R=16, N=5, M=1, Fin=6400, R2=2, Fcut=80, ntaps=60 Ĭfir = cic_comp_design(R,N,M,Fin,R2,Fcut,ntaps) įIR compensation filter (impulse response) Global filter (CIC and compensation FIR)Īs you can see on the above figure, the compensation filter (red curve), Number of taps for the compensation FIR filter: 60.Cut-off frequency for compensation filter: 80 Hz (the FIR compensation stage will work at 400 Hz).Compensation filter decimation factor (R2): 2 (sampling frequency at the end of the chain: 200 Hz).CIC decimation factor (R): 1/16 (sampling frequency at the output of CIC filter: 400 Hz).In this example, we wish to decimate a signal according to the following The following diagrams are then plotted: CIC filter / before decimation: frequency response CIC filter / after decimation: frequency response and et frequency aliasing Design of a CIC compensation filter One can compute the frequency response and the frequency aliasing at the output of the CIC filterīy simply calling the following command: cic_analysis(R, N, M, Fin) Usage examples Frequency response computingįrom the three parameters R, N and M of a CIC filter (for more completeĮxplanations on these notations and the internal working of a CIC filter, Filters are pixel-to-pixel image transformations, which not only depend on the gray level of a given pixel, but also on the value of the gray levels of neighboring pixels. Now all the CIC design functions are available on the SCILAB console (see API section for the documentation).On the scilab console, type exec(".path to the toolbox.\loader.sce").Download the CIC design mini-toolbox, and unzip it.If not already done, install SCILAB (free numerical computing software).This SCILAB mini-toolbox enables you to evaluate (in terms of frequency response)ĬIC filters, to design what is called a compensation filter, and to simulate the CIC Integrators and comb filters are necessary), which enable them to be used even ![]() The advantage of these filters is that they are particularlyĮfficient in terms of complexity (no mulmultiplication, only (reduction of the sampling rate: oversampled acquisition systems, To change the sampling frequency in a big ratio, for instance for decimation ![]() CIC filters are very useful in applications where one needs
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