The scientist and engineer's guide to digital signal processing

What Hacker News says

1. spacechild1Aug 2026
   Side note: Steve Smith is also the author of the excellent https://dspguide.com/
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2. anigbrowlOct 2025
   Read this (which is free): The Scientist's and Engineer's Guide to Digital Signal Processing* https://www.dspguide.com

   It's very comprehensive, but it's also very well written and walks you through the mechanics of Fourier transforms in a way that makes them intuitive.

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3. rzzztJun 2024
   There is a book on DSP (theory) by Steven W. Smith which you can read online: http://www.dspguide.com/pdfbook.htm
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4. hgomersallApr 2024
   The trite response is "get better at maths". I honestly don't mean that facetiously - the whole understanding that underpins "neat tricks" and how to use them is "maths". Once you can use said neat tricks, you are better at maths.

   So, I would suggest a better question is: where can I find an introduction to this stuff that is better aligned with my current understanding and knowledge. To that end, here's a great intro book: https://www.dspguide.com/

   I read that cover to cover when I was 17 and I would not have been described as particularly mathematically gifted.

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5. rescbrDec 2023
   It's not an article, but I found The Scientist and Engineer's Guide to Digital Signal Processing book to be very comprehensive. There are a couple of chapters on applications, but not much code.

   http://www.dspguide.com/pdfbook.htm

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6. rmnclmntJun 2023
   Yeah good times! The other day I was browsing for the 999th time Steve Smith's book "The Scientist and Engineer's Guide to Digital Signal Processing"[1] and stumbled upon the chapter on NN[2]: I remember ready this when I was a student I could make sense of it and why it worked, but reading it 15 years later I find it is explained so clearly compared to other resources! (maybe experience is playing in my favor too)

   You got a BASIC code snippet for training and inference and mos of all, there is an explicit use-case for digital filter approximation! At the time NN were treated as a tool among other ones, not a "answer-to-everything" type of thing.

   I know Deep Learning opened new possibilities but a lot of time CNN/RNN/Transformers are definitely not needed: working on the data instead and using "linear" models can go really far (my 2 cents)

   [1]: https://www.dspguide.com [2]: http://www.dspguide.com/ch26.htm

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