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 generate some data (such as
to read and write Wave files (
to represent or construct (multi channel) Wave files (
to transform Wave objects (
play Wave objects are used.
Other functions and classes are available to
calculate several periodograms of a signal (
to estimate the corresponding fundamental frequencies (
to derive the corresponding notes (
and to apply a
Now, the melody and corresponding energy values can be plotted using the function
A next step is the quantization (
quantize) and a corresponding plot
quantplot) showing the note values for binned data.
Moreover, a function called
(and a data-preprocessing function
can prepare a data frame to be presented as sheet music by
postprocessing with the music typesetting software LilyPond.
Of course, print (show), plot and summary methods are available for most classes.
Uwe Ligges <firstname.lastname@example.org> with contributions from Sebastian Krey, Olaf Mersmann, Sarah Schnackenberg, Andrea Preusser, Anita Thieler, and Claus Weihs, as well as code fragments and ideas from the former package sound by Matthias Heymann and functions from ‘rastamat’ by Daniel P. W. Ellis. The included parts of the libmad MPEG audio decoder library are authored by Underbit Technologies.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
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: Wobj <- bind(sine(440), sine(220)) show(Wobj) plot(Wobj) # it does not make sense to plot the whole stuff plot(extractWave(Wobj, from = 1, to = 500)) ## Not run: play(Wobj) # listen to the sound ## End(Not run) tmpfile <- file.path(tempdir(), "testfile.wav") # write the Wave object into a Wave file (can be played with any player): writeWave(Wobj, tmpfile) # reading it in again: Wobj2 <- readWave(tmpfile) 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): ## Not run: Wobjm11s <- extractWave(Wobjm11) ## End(Not run) # or extract some values reproducibly Wobjm11s <- extractWave(Wobjm11, from=1000, to=17000) # 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) # or a spectrogram image(WspecObject, ylim = c(0, 1000)) # 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) # apply some quantization (into 8 parts): qnotes <- quantize(snotes, WspecObject@energy, parts = 8) # an plot it, 4 parts a bar (including expected values): quantplot(qnotes, expected = rep(c(0, -12), each = 4), bars = 2) # now prepare for LilyPond qlily <- quantMerge(snotes, 4, 4, 2) qlily