Introduction to Digital Filters (2024)

https://ccrma.stanford.edu/~jos/filters/

by ofalkaed • 1 year ago

108 23 1 year ago

ccrma.stanford.edu

o11c• 1 year ago

Title misses important context: "for sound"

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.

sfpotter• 1 year 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.

munificent• 1 year ago

For any one-dimensional signal, honestly.

Audio is just the most common use case.

Blackthorn2• 1 year ago

Without loss of generality.

monster_truck• 1 year ago

Do you think that's air you're breathing

stapedium• 1 year ago

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.

iainctduncan2• 1 year ago

Check the rest of his writing, I'd be surprised if it's not covered somewhere!

vmilner• 1 year ago

He has a joint paper using one here:

https://www.dafx17.eca.ed.ac.uk/papers/DAFx17_paper_21.pdf

khiner• 1 year 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:

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...

florilegiumson• 1 year ago

Thank you: these are excellent.

em3rgent0rdr• 1 year ago

My god, animating convolution makes it so much easier to understand than having a professor draw the process on a chalkboard back in the day.

djmips• 1 year ago

There's also a nice 3blue1brown video on the subject

iainctduncan2• 1 year ago

The Julius Smith books are some of the most respected resources in the audio world. Here is a page linking to way more.

https://ccrma.stanford.edu/~jos/

anyfoo• 1 year ago

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".

ktanvr1• 1 year 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!

https://kewltools.com/digital-filter

Llamamoe3• 1 year ago

I wish there was something like this but for working with arrays of values. I want something that works on frequencies like 1,2,3,4,6,8, not "0.25 to 0.375". I don't even know what that would mean in the context of an array of discrete values.

CamperBob22• 1 year ago

Your question is an excellent example of why skipping all that math wasn't a good idea. (The answer literally goes all the way back to the Heisenberg uncertainty principle.)

You don't need to be able to regurgitate it all on a test, but you must be comfortable with the general ideas behind the DFT and what motivates them.

Llamamoe3• 1 year ago

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.

Blackthorn2• 1 year ago

There are tons and tons of libraries for just running filters. scipy.signal has basic filter construction methods.

This resource is for learning the why and the how, which makes the math rather important.

Archit3ch• 1 year ago

As an aside, for anyone interested in _analog_ filters, professor Lanterman has you covered: https://www.youtube.com/watch?v=Pwe3DwoBP8g

Introduction to Digital Filters (2024)

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