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 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.
Vast majority of this book covers DSP in very broad generality, much akin to what you would see in an undergrad EE course on the topic. Compare with Oppenheim and Schafer. Different focus but much of the same content.
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://kewltools.com/digital-filter
https://ccrma.stanford.edu/~jos/
Audio is just the most common use case.