Overview:

Automatic music generation is part of the broader area of algorithmic composition, which is part of the field of artificial intelligence, yet research on the topic today is still brooding in its infancy. Existing works are lacking in musical integrity. There are three significant structure to proper music: harmonic structure, rhythmic structure, and recurrent structure. Ironically, models based on recurrent neural networks, at the time of writing, are restricted to monophonic melodies and also do not capture reasonable rhythmic structure and are therefore weak. L-grammar systems and evolutionary systems perform worse still. This model builds upon the idea of a stochastic model based on Markov transitions that can handle all three types of structures.

This project contains the full code I used for my 2015 undergrad senior thesis that earned me a publication at the AIIDE conference.

Required python 2.7 modules:
    - IPython
    - numpy
    - matplotlib
    - sklearn
    - skimage
    - music21

Run cmm.py

python cmm.py

It produces a file named output.mid once it finishes running. The output's score is also generated in musicXML, which can be viewed using notation software such as Finale NotePad.

Configuring: (scroll towards the end of cmm.py) Edit pieces in cmm.py to select source pieces:

pieces = ["mid/hilarity.mid", "mid/froglegs.mid", "mid/easywinners.mid"]

Toggle between two modes: mixture or segmentation (I haven't had a chance to figure out a way to combine the two):

segmentation = True
all_keys = False

• The cached folder holds cached computations. Delete them if they are causing you problems. Note that recalculating stuff will take a while.

• The software requires that all midi files in mid are properly quantized (i.e. each note type has a regular number of ticks).

analyze.py

This file sets up infrastructure for the similarity measure using a classifier that compares two music segments and gives a score between 0 and 1

Defines a training model, trains it, and saves it in a pickled file in cached/

chords.py

Implements some tools to help identifying chords

1. Chord Templates
2. Chord Generator
3. Classifier

cmm.py

Implements the Generic Markov Model, Statechains, NotesStates, and relevant functions

data.py

Contains data structures to facilitate working with MIDI tracks, defines edit distance and other functionality

experiments.py

patterns.py is too big of a file, so this is a wrapper to help with segmentation

the file probably needs a better name

midi.py

written by Daniel Eisner, parses a raw MIDI file and returns a list

contains bug fixes written by me

mixture-plot.py

script to produce a mixture plot

patterns.py

implements the segmentation algorithm

recurrence-plot.py

script to produce a recurrence plot

similar_sections.py

defines training data that help train a measure of similarity between two segments

used by analyze.py

playback.py

module that houses a few helper functions for realtime playback

playback-demo.py

PROOF OF CONCEPT - real time dynamic playback

Loops over MIDI events and sends them to a MIDI channel

It polls a local file called trigger_file to toggle between two .mid files: To trigger a toggle, just put something in the file as follows:

echo "blah" > trigger_file

(only tested on OSX)

Requires simplecoremidi package for python: https://github.com/sixohsix/simplecoremidi, and mido package for python: https://mido.readthedocs.org/en/latest/

$ pip install simplecoremidi
$ pip install mido

To receive those MIDI events and be able to play them, use a DAW (I use REAPER which is 'free') and have it receive from the "simple core midi source" input and play to a virtual instrument

stream-cmm.py

Stream and play notes from a Markov Model constructed from a midi file, one state at a time.

stream-dynamic.py

Stream and play notes from Markov Models constructed from midi files, one state at a time.

Accepts external signals to schedule a change in its underlying Markov Model

To schedule a random Markov model, run:

echo "random" > trigger_file