slides.md

Data-Driven Music in R

Brian Abelson

Why would you want to make music in R?

Reason 1: Your data is in R and you don't know any better

Why would you NOT want to make music in R?

Let's face it...

The non-ducttape way...

• Ari Russo's work with MIDI in Ruby.
• SuperCollider
• Overtone

Existing packages:

• tuneR
  • sound i/o and manipulation
• seewave
  • graphical representations of sound
• playitbyr
  • data sonification w/ ggplot2 syntax (kind of abstract)

ddr - data-driven rhythms in R

• built-in instrument and drum libraries (lists of wav files, easy to make your own!)
• mapping data to common scales and chords.
• simple sound manipulation (chop, reverse, pitch, loop, combine)

Remember...

 ddr is severely limited by its reliance on wav manipulation.

Basics

``` piano$C3 piano[["C3"]] ``` ``` chop(piano$C3, bpm=100, count=1/8) ``` ``` reverse(piano$C3) ``` ``` chop(pitch(piano$C3, -36, FALSE), bpm=100, count=2) ``` ``` loop(chop(piano$C3, bpm=100, count=1/8), 16) ``` ``` chord(C3, piano, "maj", bpm=100, count=4) ```

Sound Sequencing - Call Me Maybe

c1 <- chord(A4, sweeplow, "maj", bpm=119, count=1)
c2 <- chord(E4, sweeplow, "maj", bpm=119, count=1)
c3 <- chord(B4, sweeplow, "maj", bpm=119, count=1)
c4 <- chord(C.4, sweeplow, "min", bpm=119, count=1)
wavs <- list(c1, c2, c3, c4, roland$HHC, roland$TAM, roland$HHO, roland$BD1, roland$SD1)

A <- c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0)
E <- c(0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0)
B <- c(0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0)
C.m<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
H <- c(0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,1,1)
T <- c(0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0)
O <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
K <- c(1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0)
S <- c(0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0)
seqs <- list(A, E, B, C.m, H, T, O, K, S)

callmemaybe <- sequence(wavs, seqs, bpm=59.5, count=1/16)
play(loop(callmemaybe, 4))

Randomly Generated Drums

``` wavs <- list(roland$HHC, roland$TAM, roland$HHO, roland$BD1, roland$SD1)

H <- rnorm(32, mean=0.5, sd=0.15) T <- rbinom(32, 1, prob=0.05) O <- rbinom(32, 1, prob=0.075) K <- rbinom(32, 1, prob=0.2) S <- rbinom(32, 1, prob=0.3) seqs <- list(H, T, O, K, S)

drum_loop <- sequence(wavs, seqs, bpm=59.5, count=1/16) play(loop(drum_loop, 4))

</a>


### Data Sonification
Let's use ChickWeight - Iris is so played out...
<a target="_blank" href="examples/chicks.wav">

data('ChickWeight') cw <- ChickWeight

chicks <- arpeggidata(sqrt(sw$weight), blip, scale="Emajor", bpm=200, count=1/32) play(chicks)

</a>


### [FMS Symphony](http://fms.csvsoundsystem.com)

bpm <- 280 ct <- 1/4

rate <- arpeggidata(fms_data$rate, sinewave, low_note="", high_note="", descending = FALSE, scale="Cmajor", remove=NULL, bpm=bpm, count=ct) writeWave(rate, "rate.wav")

ceil <- arpeggidata(fms_data$dist_to_ceiling, sinewave, low_note="", high_note="", descending = TRUE, scale="Emajor", remove=NULL, bpm=bpm, count=ct) writeWave(ceil, "ceiling.wav")

gen_chords <- function(z) { if (z < 0) { if (z <= -0.5) { c <- chord(A3, sinewave, "min", bpm=bpm, count=ct) } else { c <- chord(A4, sinewave, "min", bpm=bpm, count=ct) } } else { if (z >= 0.5) { c <- chord(C4, sinewave, "maj", bpm=bpm, count=ct) } else { c <- chord(C3, sinewave, "maj", bpm=bpm, count=ct) } } return(c) }

chords <- llply(fms_data$z_change, gen_chords, .progress="text") bind_list_of_waves <- function(x, y) { bind(x, y) }

reduce_waves <- function(list_of_waves) { Reduce(bind_list_of_waves, list_of_waves) } chords <- reduce_waves(chords) writeWave(chords, "chords.wav")

## Thanks!



## FYI:
- Thomas Levine, data superhero, is available for hire.
<br></br>
- Our band, [CSV Soundsystem](http://www.csvsoundsystem.com/) is available to play your:
    * Shareholder meetings.
    * Christmas parties.
    * Sweaty warehouses.