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the predominant stage of the speaker is the audio signal processing stage. That's a questionable statement. Transducers, enclosure design, physical management of diffraction and dispersion are arguably more important and can not be fixed with signal processing (analog or digital). Keep in mind that the speaker emits a complicated 3-dimensional sound field ...

3

That depends on what you want to plot: The response of the continuous time system or the response of a sampled discrete time system, which will always depend on the choice of sample rate. Your system is not band-limited, so it can't be sampled without some amount of aliasing. Do I just evaluate s at iω If you just want to look at the approximate frequency ...

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I was wondering, does this property also holds for the transposed direct form 1? It does not. In fact in terms of numerical performance, transposed direct form 1 is outright terrible. It helps to look at transfer function from the input to the state variables. For single biquad we have H(z) = \frac{b_0+b_1z^{-1}+b_2z^{-2}}{a_0+a_1z^{-1}+a_2z^{-2}} = A(z)\...

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Why is the Chebyshev2 plot so different from the others? Because something is really wrong with your code or plotting routine. The poles of Butterworth filters are NOT real and it should look like this

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If you do a synchronous capture of (1) the 50 Hz line carrier without your signal, and (2) your signal, you may be able to subtract, or cancel out, any 50 Hz carrier in your signal, which will likely have a different phase and bandwidth than any independent modulated 50 Hz spectrum in your signal.

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[Nota: the bandwidth makes me think about field seismic signal, which has literature on 50 Hz and 60 Hz power line removal] The question revolves on how much your signal differs from a sine at 50 Hz. On the one hand, imagine that your signal is a pure 50 Hz frequency. It would be in indistinguishable from the power line disturbance. On the other hand, if it ...

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If you have the symbolic math toolbox, then you should be able to follow the instructions in Chapter 3 here. I tried to do this for your example: syms t tau a = 10; omega = 2*pi*100; f = a*exp(-a^2/(4.0*t))/(2.0*sqrt(pi)*t^(3.0/2.0)); g = exp(1i*omega*t); z = int(subs(f,tau)*subs(g,t-tau),tau,-inf,inf); z = simplify(z); figure(1) ezplot(f) but the ...

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Ideas: If 50 Hz envelope is smooth, you may be able to estimate (desired) signal power as recorded power less distubance power (spectral subtraction). For some applications this might be enough, but you will have phase distortion in your desired signal. Assume that the contribution of «mains hum» is highly stationary, while the signal that you want (e.g. ...

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As you already now a 1st order CIC filter is identical to a moving average filter. Lets consider the decimation factor to be 2 and having the following time series: input = 10 11 12 13 14 15 Lets have a look at the convolution with the fir filter h=[1 1] 0 10 11 12 13 14 15 1 1 0 0 0 0 0 The first output sample will be 10 0 10 11 12 13 14 15 0 1 1 0 ...

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Lots of excellent answers here, to which I'm going to add two depressing ones: If your signal is absolutely indistinguishable from line hum, then you can't filter out one without filtering out the other. You have to ask yourself (and find an answer) what the difference is between line hum and your desired signal. The best way to filter out power line hum ...

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