On Sunday, 10 April 2016 at 14:57:03 UTC, hilop wrote:
On Saturday, 9 April 2016 at 14:15:38 UTC, Nordlöw wrote:
Has anybody more than I thought about representing the sample
rate of a sampled signal collected from sources such as
microphones and digital radio receivers?
With it we could automatically relate DFT/FFT bins to real
frequencies and other cool stuff.
Maybe we could make it part of the standard solution for
linear algebra processing and units of measurement in D.
Destroy.
The magnitude is not hard to represent for a buffer.
let's say you have a FFT of 512 sample, each bin N represent a
sub-sinusoid of a frequency given by (SR/(512*2)) * N. Then
you've got the power of this sub-sinusoid with Hypoth(bin.real,
bin.imag).
Since amplitude perception is not linear, the Y (A to db, from
.0f->.1f to -100f->0.0f scale must be adjusted.
Since the frequency neither the X scale also (frequency to
pitch, from 0->22050 to 0->127). (I don't remember the formula
right now but those two converters could be part of the unit
framework.)
Now to make the things properly (which means avoiding the
artifacts, aka the aliasing or the spectrum folding, due to the
cut at the buffer edge), the buffers must be multiplied by a
windowing function (e.g hanning, hamming, etc) and overlapped
(to maintain the original power spectrum).
Believe me or not but we are living in a world where it's easy to
get the information, but very few people are able to understand
the information.
Technician vs Analyst.
