Nyquist-Shannon Sampling + Shazam

Nyquist–Shannon sampling theorem
    MyNutShellDefinition: With twice the frequency of the highest contributing sine wave frequency, you can accurately reproduce your signal. 


- Following the Shazam Algorithm - 
The Concept:

If you put the constellation map of a database song on a strip chart, and the constellation map of a short matching audio sample of a few seconds length on a transparent piece of plastic, then slide the latter over the former, at some point a significant number of points will coincide when the proper time offset is located and the two constellation maps are aligned in register. 

The Implementation:

Each anchor point is sequentially paired with points within its target zone, each pair yielding two frequency components plus the time difference between the points (Figure 1C and 1D).  These hashes are quite reproducible, even in the presence of noise and voice codec compression.  Furthermore, each hash can be packed into a 32-bit unsigned integer.  Each hash is also associated with the time offset from the beginning of the respective file to its anchor point, though the absolute time is not a part of the hash itself.

Questions:

What constitutes a constellation point? (There may/will be multiple per bin. We will use one.)

MISC: 

DFT Walkthrough - (think I saw his stuff in Modeling&Post)

Using the sampling rates etc suggested here


In some audio processing approaches, a visual representation of the signal is created and analyzed. (Note to self - check out Hough Transform )

After presentations, jLaster mentioned Bloom Filters, which rapidly tell you whether an element is present in your set. Might be useful for a preliminary test on very large data sets. 

RANDOM : 
What was that, Eleanor dear? - marthakelly's hilarity
Beautiful Sudoku Code - recommended by jczetta