Crash course on turning engineering formulae into computer code? I'm trying to teach myself about the basics of feature extraction from digital signals. Specifically, I want to try monophonic then polyphonic pitch recognition. I don't have any university level math or computing science experience (I'm self-taught), so I find the formulas in many texts and websites to be a road-block because I don't understand what most of the Greek symbols mean. E.g. I had to search and find that $ \Sigma $ is called Sigma and that it means "summation"...

As a self-taught recording engineer I have a decent understanding of what needs to happen from a high level:


Separate digital signal into component frequencies
Analyse the relative strength of each frequency bin
Evaluate the strongest frequencies and compare against known values to determine most likely chord


When I start trying to do any reading about these theories I can't translate it into code because I have no idea what the mathematical explanations are trying to tell me.

Maybe this is a question for a math forum, but I figured that a group of DSP enthusiasts and experts could help me fill in the gaps between my coding knowledge and my basic understanding of sound. I get that a Fast Fourier Transform takes a signal and transforms it from the time domain into the frequency domain, but I don't understand the process when I see it laid out in mathematical terms.

How does someone in my position move forward? What kind of resources exist to help someone with high-school algebra (but a "can-do" attitude!) learn enough to get comfortable with basic signal processing? Is there a less "math-intensive" way to gain an understanding of what needs to happen that could lead to a better understanding of the symbols and formulas commonly used?
I don't usually recommend this series, but Linear Algebra For Dummies might be right for you. Chapter 19, for example, covers all the Greek symbols.

You should also complement this with an introductory book on DSP, like Applied digital signal processing : theory and practice and DSP for audio An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics