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If a downsampling zoom factor is not integer, as is the case in your example (1/s=4/3), the procedure is more involved and comprises first interpolation followed by smoothing and then decimation. Also, when constructing Gaussian filters of finite support, you should understand that you construct just a FIR approximation and, with a finite filter length, the ...


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Doing the summation is the necessary low pass filter step prior to decimation, and an ideal decimation approach when the noise is white (evenly spread across frequency). What this does is ensure that every signal component of every sample is included while the noise gets reduced through averaging (summing is averaging just without the scaling by dividing by ...


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I would not recommend either default filter in the signal.decimate function for decimation operations. The "non-deterministic" effects if observed may be due to noise aliasing. An FIR filter designed using the least squared algorithm (scipy.signal.firls) is much better suited given it will have the least noise overall in a least squared sense for ...


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I want a pre-processing algorithm to downsample audio in such a way that it is fully deterministic All coded algorithms are deterministic unless they are specifically designed not to (with random seeds) or there is inconsistent initialization. That's the fundamental idea behind regression testing. So yes: Unless you do something wrong any down-sampling ...


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These algorithms are all deterministic. Whether a chebyshev design is what your decimation needs is a bit of a broad question. Probably you're just as good off designing a low-pass filter with scipy.firdes, and then using that to filter your input before just dropping the amount of samples (e.g, every odd one) you want to ignore.


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