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.
iainctduncan · 4h ago
The Julius Smith books are some of the most respected resources in the audio world. Here is a page linking to way more.
And not just for audio. In fact, I don't care about audio that much, and they're still some of my most treasured technical books (I have them in print form, and still reference them online pretty regularly).
Those changed my life, in a sense. Not my professional life, but outside of work it led me down a deep rabbit hole into mathematics, digital signal processing, and even analogue electronics and some light RF engineering. (This is not relevant to my professional life, since I started to take great care not to make any more of my hobbies my job.)
I spent endless hours thinking about this stuff on my commute, and hunched over Matlab.
The other book I recommend is Richard G. Lyons "Understanding Digital Signal Processing".
khiner · 5h ago
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:
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.
iainctduncan · 4h ago
Check the rest of his writing, I'd be surprised if it's not covered somewhere!
ktanvr1 · 3h ago
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!
A lot of it applies to software defined radio processing as well, other than tending to work in real vs complex, but you can always do either.
munificent · 8h ago
For any one-dimensional signal, honestly.
Audio is just the most common use case.
sfpotter · 8h ago
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.
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.
https://ccrma.stanford.edu/~jos/
Those changed my life, in a sense. Not my professional life, but outside of work it led me down a deep rabbit hole into mathematics, digital signal processing, and even analogue electronics and some light RF engineering. (This is not relevant to my professional life, since I started to take great care not to make any more of my hobbies my job.)
I spent endless hours thinking about this stuff on my commute, and hunched over Matlab.
The other book I recommend is Richard G. Lyons "Understanding Digital Signal Processing".
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
Audio is just the most common use case.