This document discusses algorithmic music composition and computational musicology using Python. It introduces Music21, a Python toolkit for computational musicology that allows analyzing musical scores and corpora. It describes strategies for algorithmic composition, including data-driven approaches like Markov models and expert systems using genetic algorithms and formal grammars guided by musical rules. Markov models are discussed as a probabilistic method for generating music based on transitions between notes, rhythms, or sections in prior musical data. Expert systems are also covered for structuring musical form, transforming material, and determining musical preferences.

1. Algorithmic Music Composition
and Computational Musicology in
Python
Halley Young
New York University
9/13/15

2. Goals of Computational
Musicology
 Use algorithms to analyze the form,
harmonies, rhythms, melodies, and
arrangement of musical data
 Use a variety of strategies (both data-driven
and non-data-driven) to try to produce music in
a given style

3. Music21 – a toolkit for computational
musicology in Python
 Score model
 Insert Note/Chord/Rest object into a Part
object, insert Part object into Score object
 Can write to MIDI, MusicXML, or PDF
 Includes corpus of musical data (Bach,
Monteverdi, Handel) to be read into memory
and analyzed

4. Strategies for Algorithmic
Composition
 Data-driven:
 Markov Models
 Neural networks
 Genetic Algorithms with fitness determined by
similarity to pre-specified pieces
 Expert systems:
 Genetic algorithms with fitness determined by
adherence to musical rules
 Formal grammars
 Stochastic processes guided by musical rules

5. Markov Models
 Probabilistic model based on prior data:
Generate a table of how often previous
transitions between two states happened, and
then randomly choose transitions based on the
table
 Can use probabilities of notes following notes,
rhythms following rhythms, chords following
chords, or sections following sections

6. Expert Systems
Musical Forms - setting up the structure of the piece
Musical Transformations - creating new material out
of pre-existing material
Musical Preferences - determining whether what
was created sounds good

7. Machine Learning and
Computational Musicology
 Automatic Key Detection
 Automatic Harmony Detection
 Automatic Music Classification

8. Questions?
 Visit mit.edu/music21 for details and
installation info