terry9,
Excellent catch on infinite series, but also easily addressed. As we are dealing with audio, there is effectively no information below 10Hz, and some would argue 20, but let’s say 10Hz. For that reason, any real single data set, i.e. a song file, can be modelled as an infinite series as there is a maximum rise time and minimum fall time at beginning and end, hence you can "set" all data outside to 0 (whatever your 0 is) for all points when applying the theorem. Any "errors" in bit level would be in the silence at the beginning and end of the track. In some ways, this is like a natural windowing function.
There are lots of papers, proofs, course books, material, etc. that goes into detail, including size of error when you don’t have an infinite series, which in a practical audio case, would be much smaller than other error sources.
If you want to play with "math", GNU Octave is a free-ware version of Mathcad (not as graphical) and would let you simulate any of these concepts.
Excellent catch on infinite series, but also easily addressed. As we are dealing with audio, there is effectively no information below 10Hz, and some would argue 20, but let’s say 10Hz. For that reason, any real single data set, i.e. a song file, can be modelled as an infinite series as there is a maximum rise time and minimum fall time at beginning and end, hence you can "set" all data outside to 0 (whatever your 0 is) for all points when applying the theorem. Any "errors" in bit level would be in the silence at the beginning and end of the track. In some ways, this is like a natural windowing function.
There are lots of papers, proofs, course books, material, etc. that goes into detail, including size of error when you don’t have an infinite series, which in a practical audio case, would be much smaller than other error sources.
If you want to play with "math", GNU Octave is a free-ware version of Mathcad (not as graphical) and would let you simulate any of these concepts.