科学与数学dspguide.com编辑精选
科学家与工程师的数字信号处理指南
The Scientist and Engineer's Guide to Digital Signal Processing
2010 — 2022，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这是数字信号处理经典教材《The Scientist and Engineer's Guide to Digital Signal Processing》的在线 PDF 页面，系统讲解 DSP 的基础概念与方法。评论普遍把它视为面向工程师和爱好者的入门/查阅资源，并提到全书可在线免费阅读。
为什么经久不衰：它把数字信号处理讲得系统又相对易懂，而且免费在线，多年来一直是入门与查阅的经典资源。
HN 评论精选
认为回避复数会令 DSP 更难懂，复数让混频和复基带处理更简洁。
我强烈反对因为“太复杂、多数科学家和工程师没时间学”就回避复数的做法。先说清楚：我受的是物理训练，所以有偏见（也会坚持用 i 而不是 j）。比如不用复数做混频就是一堆三角恒等式；而用复数，exp(2πif1t)exp(2πif2t)=exp(2πi(f1+f2)t)，清楚得多。我认为 DSP 的美妙之处正是可以直接处理复基带信号，而不是拆成 I/Q 再做各种掩盖调制/解调本质的操作。作者说多数教科书会用复数，我不怀疑；但我找到的许多在线资料都回避复数，我通常把它们转回复数后反而更容易理解（数学少掉四分之三）。
gabia查看英文原文
I strongly disagree with the approach of avoiding complex numbers because its "so complicated that most scientists and engineers can't spare the time to learn or use it". Full disclosure though: my training is physics, so I'm pretty biased (and will use 'i' instead of 'j' with great prejudice). E.g. frequency mixing without complex numbers is a mess of trig-identities, and (at least to me) it's not clear at all why multiplying various terms should eventually result in the desired outcome. Whereas with complex numbers: exp(2pi i f1 t) exp(2pi i f2 t) = exp(2pi i (f1+f2) t). Other concepts are similar. In my mind the beauty of DSP is one can work directly with complex base-band signals, rather than splitting into I and Q and then doing all sorts of shenanigans which obscure the underlying modulation/demodulation schemes - which are often quite simple but appear complicated once physical components are required (vs doing it all in software). From a historical perspective of course that stuff is all very interesting, but less helpful if you want to learn the core concepts. The author also says most textbooks do use complex numbers which I don't doubt, but most of the resources I've found online avoid them, I usually find I can understand what's going on much better after converting it back (and hence eliminating 3/4 of the maths). If this interests anyone - I started making a little tutorial on GMSK out of frustration about this issue, currently no actual notes, just implementations (which may not be perfect! I am not an expert) https://github.com/gabi-a/DSP_SatComms_Tutorial/blob/main/Signals.ipynb*
用图像→音频→图像的实验说明书中概念（复平面）的实用威力。
就是这本书让我能写出一个方案：解析图像并合成其音频等价物，再听音频合成回输出图像；当输出图像与输入图像匹配时，工作完成——这就是复平面的力量。
AtomicOrbital查看英文原文
This is the book which enabled me to code up a solution to parse an image to synthesize it's audio equivalent ... then to listen to the audio to synthesize it's equivalent output image ... job done when output image matched input image ... power of the complex plane
称其为多年常用的经典资源，并补充另一个友好学习站点。
这是我多年来使用并见别人使用的经典资源。它帮助过音乐从业者起步，也帮助过爱好者。另一个很友好的资源是 https://www.katjaas.nl/home/home.html
monetus查看英文原文
This is a canonical resource I've used and seen used for years. It has helped kick off musical careers, as well as hobbyists. Another great approachable resource https://www.katjaas.nl/home/home.html
强调卷积/冲激响应是线性系统建模的核心，前几章对多领域都有用。
对任何对“频率”感兴趣的人都是极好的书。整个领域建立在一个简单而精彩的想法上：用卷积和冲激响应来建模任何线性系统。我鼓励你至少读前几章；它对很多不同领域都有用。
tsomctl查看英文原文
An excellent book for anyone interested in anything that has a frequency. The whole field is based on a simple but brilliant idea: using convolution and an impulse response to model anything linear. I encourage you to at least read the first several chapters; it is useful to a lot of different fields.
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