Schon länger her, aber ich finde die folgende Erklärung zum Thema DFT
<-> DCT gut. Quelle:
http://wapedia.mobi/en/Sine_and_cosine_transforms
Eine gute Erklärung zum Thema DFT <-> DCT gibts auch hier:
http://www.fftw.org/fftw3_doc/Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029.html#Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029
Gute Literatur zum Thema MDCT gibts hier:
"On the Relationship Between MDCT, SDFT and DFT", Wang et al.
http://www.comp.nus.edu.sg/~wangye/papers/On%20the%20Relationship%20Between%20MDCT,%20SDFT%20and%20DFT.pdf
Umrechnung zwischen MDCT und DFT:
"Improved Coding Techniques Using Estimated Spectral Magnitude and
Phase Derived from MDCT Coefficients", Cheng et al.
http://www.wipo.int/pctdb/images4/PCT-PAGES/2005/322005/05073960/05073960.pdf
"Packet Loss Concealment for Audio Streaming Based on the GAPES
Algorithm", Ofir & Malah
http://sipl.technion.ac.il/new/Research/Publications/Graduates/Hadas_Ofir/AES_Convention/Article/AES_118_Article_327_Ofir_and_Malah_final.pdf
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In mathematics, the Fourier cosine transform is a special case of the
continuous Fourier transform, arising naturally when attempting to
transform an even function. Consider the general Fourier transform:
We may expand the integrand by means of Euler's formula:
or, written as the sum of two integrals:
Now notice that if we assume f(t) is an even function, the product
f(t)cosωt is also even whilst the product f(t)sinωt is an odd function.
Since we are integrating over an interval symmetric about the origin
(i.e. -∞ to +∞), the second integral must vanish to zero, and the first
may be simplified to give:
which is the Fourier cosine transform for even f(t). It is clear that
the transformed function F(ω) is also an even function, and a similar
analysis of the general inverse Fourier transform yields a second cosine
transform, namely:
Note that the numerical factors in the transforms are defined uniquely
only by their product, as discussed for general continuous Fourier
transforms.