music21

(1)

from music21 import serial

p = [8,1,7,9,0,2,3,5,4,11,6,10]
print serial.rowToMatrix(p)

  0  5 11  1  4  6  7  9  8  3 10  2
  7  0  6  8 11  1  2  4  3 10  5  9
  1  6  0  2  5  7  8 10  9  4 11  3
 11  4 10  0  3  5  6  8  7  2  9  1
  8  1  7  9  0  2  3  5  4 11  6 10
  6 11  5  7 10  0  1  3  2  9  4  8
  5 10  4  6  9 11  0  2  1  8  3  7
  3  8  2  4  7  9 10  0 11  6  1  5
  4  9  3  5  8 10 11  1  0  7  2  6
  9  2  8 10  1  3  4  6  5  0  7 11
  2  7  1  3  6  8  9 11 10  5  0  4
 10  3  9 11  2  4  5  7  6  1  8  0

(2) We want to graphically show correlations between the length of notes and their heights using a piece coded in musicxml or humdrum format (these are from our downloaded corpora):

for work in ['opus18no1', 'opus59no3']:
    movementNumber = 3
    score = corpus.parseWork(work, movementNumber)
    
    for part in score:
        instrumentName = part.getElementsByClass(instrument.Instrument)[0].findName()
        grapher = correlate.NoteAnalysis(part.flat.sorted)    
        grapher.pitchToLengthScatter(title='%s, Movement %s, %s' % (work, movementNumber, instrumentName))

Displays 8 images, including:

This is a preview of the music21 system for computer-aided musicology being developed at MIT (Michael Scott Cuthbert, Asst. Prof., Principal Investigator; Christopher Ariza, Visiting Asst. Prof., Development Lead). We'll be using this blog to showcase some of the features of the system, and to highlight other interesting things happening in computational, statistical, and other empirical methods in musicology.

Although computers have transformed how we listen to, obtain, compose, and notate music, they have not fundamentally changed how we research and analyze music. Though many computer databases have been created for musicology, they are not well adapted for sophisticated music queries. For instance, melodies can be found if exact matches exist. But melodic variations such as the repetition of a phrase or a change in embellishment are extremely common, yet cause searches to fail. More complex investigations, such as finding all melodies that imply a particular underlying harmony, can barely begin to be created with existing software packages. The lack of relevant software for analyzing music hampers scientific attempts to understand what we listen for and how we process what we hear; these activities are little understood despite music’s nearly universal presence in our daily lives.

The music21 project at M.I.T. will give to the music community the set of tools it needs to conduct sophisticated musical and statistical analysis using modern programming techniques. The software framework, written in Python, manipulates music as a collection of symbolic data, such as pitch names and note durations, that can then be classified as higher level musical structures according to the style, region, or period being studied.

Music21 focuses specifically on the manipulation of symbolic music data: it leaves to the many preexisting open-source and proprietary software packages the notation and audio playback of scores (the two areas where computer-aided music research is most developed). By focusing on the points of greatest need to musicology, the framework will give rapid results within a short timeframe.

The music21 framework will be freely available in early 2010 under the LGPL open source license.

Some demos:

from music21 import *

n = note.Note("F#4")
n.quarterLength = 3

a = stream.Stream()
a.repeatAdd(n, 20)  # add 20 copies of n

a.insertAtOffset( 0, meter.TimeSignature("5/4")) 
a.insertAtOffset(10, meter.TimeSignature("2/4")) 
a.insertAtOffset( 3, meter.TimeSignature("3/16")) 
# N.B.they don't have to be inserted in order 

a.insertAtOffset(20, meter.TimeSignature("9/8")) 
a.insertAtOffset(40, meter.TimeSignature("10/4"))
a.insertAtOffset(50, meter.TimeSignature("29/32"))
a.show()