Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
# read, downsample, clip, mel spec, normalize and remove noise
melspec <- function(x, start, end){
mp3 <- readMP3(filename = x) %>%
extractWave(xunit = "time",
from = start, to = end)
# return log-spectrogram with 256 Mel bands and compression
sp <- melfcc(mp3, nbands = 256, usecmp = T,
spec_out = T,
hoptime = (end-start) / 256)$aspectrum
# Median-based noise reduction
noise <- apply(sp, 1, median)
sp <- sweep(sp, 1, noise)
sp[sp < 0] <- 0
# Normalize to max
sp <- sp / max(sp)
return(sp)
}
# iterate melspec over all samples, arrange output into array
melslice <- function(x, from, to){
lapply(X = x, FUN = melspec,
start = from, end = to) %>%
simplify2array()
}
# iterate melslice over all different time windows
audioProcess <- function(files, limit = 10, ws = 10, stride = 2,
ncores = 8){
windowSize <- seq(0, limit, by = stride)
# iterate and parallelise
batches <- mclapply(windowSize, function(w){
# execute
melslice(files, from = w, to = w+ws)
}, mc.cores = ncores)
# combine output into single array
out <- abind(batches, along = 3)
# reorder dimensions after adding single-channel as 4th
dim(out) <- c(dim(out), 1)
out <- aperm(out, c(3,1,2,4))
return(out)
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.