I was hoping to see something on Kalman filters. But it was good to see info on state space analysis. Also good to see a simple example on why dynamic range compression is nonlinear. Would have been nice to see more info on what makes a system non-time invariant with examples.
Self plug: I made Jupyter notebooks for each chapter of this and the DFT and Physical Modeling books in this series, with Python animations/audio for some key concepts:
Shout out to kewltools that have a free online digital creator - the nice thing is it generates and outputs source code of the digital filter in multiple languages!
I wish there was a practical, no-math code-centric resource somewhere.
I just want to see practical examples of how to process my array of floats to extract or attenuate different frequencies(in discrete integer increments), not read walls of math equations and how to derive the discrete form of continuous algorithms over a hundred pages of dense text.
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[ 4.0 ms ] story [ 33.2 ms ] threadhttps://www.dafx17.eca.ed.ac.uk/papers/DAFx17_paper_21.pdf
https://karlhiner.com/jupyter_notebooks/mathematics_of_the_d...
https://karlhiner.com/jupyter_notebooks/intro_to_digital_fil...
https://karlhiner.com/jupyter_notebooks/physical_audio_signa...
https://ccrma.stanford.edu/~jos/
https://kewltools.com/digital-filter
I just want to see practical examples of how to process my array of floats to extract or attenuate different frequencies(in discrete integer increments), not read walls of math equations and how to derive the discrete form of continuous algorithms over a hundred pages of dense text.
This resource is for learning the why and the how, which makes the math rather important.