Evolving your own music for fun and no profit

What's evolving music?

• Take existing music
• Learn patterns from it
• Use the knowledge to make new music
• Judge your new music

Why?

• Because it's cool
• See above reason

Listen in

• Sample tracks: 1 2 3

Midi format

[midi.ProgramChangeEvent(tick=0, channel=0, data=[0]),
midi.NoteOffEvent(tick=20, channel=9, data=[42, 64]),
midi.NoteOnEvent(tick=28, channel=9, data=[38, 90]),
midi.NoteOnEvent(tick=0, channel=9, data=[42, 90]),
midi.NoteOffEvent(tick=20, channel=9, data=[38, 64]),
midi.NoteOffEvent(tick=0, channel=9, data=[42, 64]),
midi.NoteOnEvent(tick=28, channel=9, data=[42, 70]),
midi.NoteOffEvent(tick=20, channel=9, data=[35, 64]),
midi.NoteOffEvent(tick=0, channel=9, data=[42, 64]),
midi.NoteOnEvent(tick=28, channel=9, data=[42, 58]),
midi.EndOfTrackEvent(tick=0, data=[])]

MIDI stored as byte code

Python-midi to the rescue!

• Parses midi into python data structures
• Found at https://github.com/vishnubob/python-midi

Step 1: ACQUIRE MUSIC

• Use scrapy -- http://scrapy.org/
• Write crawlers to scrape sites
• Download .mid files

Step 2: Render tracks

• MIDI has to be "rendered"
• Output to .ogg files

Step 45: Markov chain everything!

Construct chains

• Make matrices for pitch, velocity, tick per-instrument
• Rows and columns are numbers
• Cells contain # of transitions from row state to column state

Note "phrases"

• Music has structure
• Phrases are basic units of it
• I have almost no music theory experience
• Let me know after if I'm wrong
• Pull out sequences of 8 notes

Making a track

• Select the instrument
• Randomly select a starting phrase
• Use markov chains to pick next phrase
• Repeat until specified length

Making a song

• Pick instruments that are good together
• Put tracks from those instruments together
• Add a tempo track

Evaluating song

• Render midi to ogg file
• Use fluidsynth, oggenc, and sox

Look at the audio

Read audio in

$$\begin{bmatrix} 2.35185598e-05 & -1.04448336e-05\\ -3.46823663e-06 & -3.73403673e-05\\ -2.69492170e-06 & -1.44758296e-05\\ 9.47549870e-06 & 2.09419904e-05\\ -2.70856035e-05 & 3.44590421e-06\\ -3.01332675e-05 & 2.74870854e-05\\ -1.44664727e-06 & 7.49632018e-05\\ -3.80197125e-05 & 2.56412422e-05\\ -5.61815832e-05 & -1.29676855e-05\\ -4.73532873e-06 & 3.69851950e-05 \end{bmatrix}$$

Feature calculation

• Calculate a bunch of features
• Split song into "bins"
• ZCR, slope, peak count, and many more
• 154 in all
• Put into matrix

Scoring songs

• Compare generated song with existing songs
• Used random forest at one point to predict
• Simpler to use cosine distance
• Lower distance is better

Semi-genetic algorithm

• Create candidate songs
• Score them all
• Pick the best ones
• Remix songs by swapping tracks
• Generate some new songs

Generations

• Evolve multiple generations
• Try to minimize distance from existing songs
• Algorithm finds local minimum quickly
• More than 3 generations gets duplicate "best" song

Ways to make it better

• Hand-label some songs, and use them for supervised learning
• Get more training data
• Generate better features
• Do more in the genetic algorithm than just remix

Open source

• Available at https://github.com/VikParuchuri/evolve-music2
• This presentation available at: https://github.com/VikParuchuri/pydataboston2015
• Want to learn how to do things like this? Check out www.dataquest.io!