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# This code is based on the Matlab implementations of PLP and Rasta
# feature calculations by Daniel P. W. Ellis of Columbia University /
# International Computer Science Institute. For more details, see:
# http://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/
dolpc <- function(x, modelorder=8){
if(!(is.numeric(x) && is.matrix(x)))
stop("'x' has to be a numeric matrix")
if(!(modelorder == as.integer(modelorder) && modelorder > 0))
stop("'modelorder' has to be a positive integer")
nbands <- nrow(x)
# px <- planFFT(2*nbands-2)
# Calculate autocorrelation
r <- apply(rbind(x, x[seq(nbands-1, 2, -1),]), 2,
function(y) Re(fft(y, inverse=TRUE))/length(y))
# function(y) Re(IFFT(y, plan=px)))
# First half only
r <- r[1:nbands,,drop=FALSE]
# Find LPC coeffs by Levinson-Durbin
levcoef <- levinson(x=r, p=modelorder)
# Normalize each poly by gain
y <- t(levcoef$a) / matrix(rep(levcoef$v, modelorder+1), nrow=modelorder+1,
byrow=TRUE)
return(y)
}
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