MDCT module for Julia
This module computes the modified discrete cosine transform (MDCT) in the Julia language and the inverse transform (IMDCT), using the fast type-IV discrete cosine tranform (DCT-IV) functions in the FFTW.jl package.
Definitions of the MDCT and IMDCT can be found, for example in the Wikipedia MDCT article. The MDCT is a linear transformation that takes 2N inputs and produces N outputs, which is designed to be applied to a sequence of 50%-overlapping blocks of a longer sequence (e.g. audio samples). Because this is non-square (fewer outputs than inputs), the IMDCT is not an "inverse" transformation in the usual sense; it only recovers the original data when IMDCTs of overlapping blocks are added (by "time-domain aliasing cancellation").
Within Julia, just use the package manager to run
Pkg.add("MDCT") to install the files.
To use the MDCT functions, simply do
using MDCT Y = mdct(X) Z = imdct(Y)
X is any numeric
AbstractVector (1d array). Currently, the
X must be a multiple of 4.
For example, suppose we make a random vector
X of length 1000 and
consider 50%-overlapping blocks of length 100 (
X[101:200], and so on). If we perform the MDCT of two
such blocks, then the IMDCT, and then add the overlapping halves of
the IMDCT outputs, we recover that portion of the original data:
X = rand(1000) Y1 = mdct(X[1:100]) Y2 = mdct(X[51:150]) Z1 = imdct(Y1) Z2 = imdct(Y2) norm(Z1[51:100] + Z2[1:50] - X[51:100])
where the last line computes the difference between the overlapped IMDCT sum and the original data, and should be around 10−15 (floating-point roundoff error).
To create a pre-planned transforms for a given size of input vector do:
using MDCT Mp = plan_mdct(X) Ip = plan_imdct(Y)
Mp are pre-planned transforms optimized to allow much
quicker subsequent transformations. To use them simply:
Xt = Mp*X Yt = Ip*Y
This module was written by Steven G. Johnson.