extraction.md

Music data extraction

The functions described in this page allow for easier extraction of data from music recordings (in the form of MIDI). A highlight of JuliaMusic is the timeseries function, which allows one to directly get gridded timeseries from arbitrary Notes structures.

Basic Data Extraction

firstnotes
filterpitches
separatepitches
combine
relpos

Advanced Data Extraction

estimate_delay
estimate_delay_recursive

Timeseries

timeseries

---

Here is an example:

using MusicManipulations, PyPlot, Statistics
midi = readMIDIFile(testmidi())
notes = getnotes(midi, 4)

swung_8s = [0, 2//3, 1]
t, vel = timeseries(notes, :velocity, mean, swung_8s)

notmiss = findall(!ismissing, vel)

fig, (ax1, ax2) = subplots(2,1)
ax1.scatter(t[notmiss], vel[notmiss])
ax1.set_ylabel("velocity")

t, mtd = timeseries(notes, :position, mean, swung_8s)
ax2.scatter(t[notmiss], mtd[notmiss], color = "C1")
ax2.set_ylabel("timing deviations")
ax2.set_xlabel("ticks")
tight_layout() # hide
savefig("timeseries.png"); nothing # hide

---

An example code that obtains the velocity of the notes with highest pitch in each bin is this one:

notes = getnotes(midi, 4)

function f(notes)
    m, i = findmax(pitches(notes))
    notes[i].velocity
end

grid = 0:1//3:1
tvec2, ts2 = timeseries(notes, f, grid)

Segmentation

segment