tuneR {tuneR} | R Documentation |
tuneR, a collection of examples
tuneR consists of several functions to work with and to analyze Wave files.
In the following examples, some of the functions
to read and write Wave files (readWave
, writeWave
),
to represent or construct Wave files (Wave
),
to transform Wave objects (bind
, channel
,
downsample
, extractWave
, mono
, stereo
),
and to play
Wave objects are used.
Other functions and classes are avaiable to
calculate several periodograms of a signal (periodogram
, Wspec
),
to estimate the corresponding fundamental frequencies (FF
, FFpure
),
to derive the corresponding notes (noteFromFF
),
and to apply a smoother
.
Of course, print (show), plot and summary methods are available for most classes.
Moreover, a function called lilyinput
can prepare a data frame to be presented
as sheet music by postprocessing with the music typesetting software LilyPond.
Uwe Ligges, ligges@statistik.uni-dortmund.de
## Not run: # library(tuneR) # in a regular session, we are loading tuneR # constructing a mono Wave object (2 sec.) containing sinus # sound with 440Hz and folled by 220Hz: x <- seq(0, 2*pi, length = 44100) channel <- round(32000 * c(sin(440 * x), sin(220 * x))) Wobj <- Wave(left = channel) show(Wobj) plot(Wobj) # it does not make sense to plot the whole stuff plot(extractWave(Wobj, from = 1, to = 500)) play(Wobj) # listen to the sound # write the Wave object into a Wave file (can be played with any player): writeWave(Wobj, "testfile.wav") # reading it in again: Wobj2 <- readWave("testfile.wav") identical(Wobj2, Wobj) # it is still the same Wobjm <- mono(Wobj, "left") # extract the left channel # and downsample to 11025 samples/sec.: Wobjm11 <- downsample(Wobjm, 11025) # extract a part of the signal interactively (click for left/right limits): Wobjm11s <- extractWave(Wobjm11) # calculating periodograms of sections each consisting of 1024 observations, # overlapping by 512 observations: WspecObject <- periodogram(Wobjm11s, normalize = TRUE, width = 1024, overlap = 512) # Let's look at the first periodogram: plot(WspecObject, xlim = c(0, 2000), which = 1) # calculate the fundamental frequency: ff <- FF(WspecObject) print(ff) # derive note from FF given diapason a'=440 notes <- noteFromFF(ff, 440) # smooth the notes: snotes <- smoother(notes) # outcome should be 0 for diapason "a'" and -12 (12 halftones lower) for "a" print(snotes) # plot melody and energy of the sound: melodyplot(WspecObject, snotes) ## End(Not run)