Hello I'm reading the following document to better understand windowing and how to use it.
"Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new at-top windows."
I try to reproduce their example (chapter 13) on audio signals but i have some questions. Just like the example i use the Hann(ing) window. with a overlab of 50% For reproducion i use python
Let say i have a typical Audio signal with a
44100 Hz and a length of
T = 5 sec.
I have a window of 0.1 sec(neglecting the nice length of
With the overlab i get an matrix of 100(Windows) X 4410 Samples.
I have two questions
Normaly i see just a single Spectrum plot (exept STFT). How come i from a my set of spectra to a single spectrum? Like what Audacity have
Averaging; summing; ... other
what is the impact of changes in time at the resulting spectrum. Let say using a (measured) sweeping signal?
Hope that somebody can help me now