Using Python to Generate Art and Sound¶
by Audrey Roy
Lots of code samples with detailed explanations. Can’t keep up with my notes but it’s awesome.
I’ve used Python to draw rainbows of different shapes and colors, Gaussian clouds, and landscapes in perspective. I’ve also used Python to create sound effects for games. This talk explores my experiments with the various Python imaging and sound tools. First, I walk the audience through implementing basic audio building blocks with the Python stdlib’s wave, math, and array modules. Then, I improve upon the code with NumPy and SciPy. Finally, I demonstrate how audio synthesis can be very similar to generative graphic art, using similar techniques to create building blocks for basic illustration.
background of the Talk¶
A few years back she was painting landscapes and got tired of repetitive techniques so she decided to write a program to do it for her
- Overwhelming variety of Python libraries for audio/graphics
- Understanding the fundamentals first
- Helps you understand your options
- Simple sound with the Python stdlib
- Numpy and Scipy
- Plotting sound arrays with Matplotlib
- Creative sound generation techniques
- Using the same tricks on graphics instead
- Very east to get started
- Other libraries can and are tricky to install
Parts she’ll be using
- Use it to open and write .vave files
- Introduced in stdlib 1.6 and hasn’t changed much since
- Using it to store data over time
- Using math.sin(x) to calculate 440 Hz audo audio samples
wave, array, math¶
Generates a 440 Hz sine wave
import array from math import sin, pi import wave SAMPLE_RATE = 44100 DURATION = 3 # TODO finish tons more code
Simplifying via a function
import array from math import sin, pi import wave SAMPLE_RATE = 44100 def note() # TODO finish coding this out
Can this be simplified further?¶
Yes via NumPy arrays!
- perfect for sound operations
# numpy.linspace(start, stop, num): >>> linspace(0, 1, 10) array() # TODO get this value #sumpy.sin(x)
Now we show the simplified example:
from numpy import linspace, int16, sin from scipy.io.wavfile import write # Using this because it's less code to use than the Wave module def note(freq, duration, amp=10000, rate=41100): # TODO add code stuff here pass
Is this music?¶
Not yet. You need chords for music!
Chords for music¶
- Simply add 2 notes of different frequencies together
- She looked up Piano key frequencies on wikipedia
# chord function def chord(): # TODO get a sample of this code pass
Using matplotlib to visualize the chord¶
She showed very nice code to plot out audio files.
Concatenate notes into sequences¶
She showed using numpy’s concatenate() function to add up arrays of sound samples.
Weaving it all together¶
- contains piano keys
- contains imports of all the notes components
Used numpy’s uniform() function to create nice distributions of frequencies and durations
Constrained the frequencies so they are humanly playable
Explained use of random.choice over numpy.choice. Chose it because numpy’s version is in beta.
- Colorful rainbow of sounds that sounds relatively pleasant to the ear
Adding Gaussian Distribution¶
- Using an algorithm to make things more centralized.
- Which blurred things so instead of a rainbow of sounds it sounded like puffy clouds. :-)
- Python API for cairo
- HTML Canvas uses cairo as well
- Showed how to use Gaussian algorithm to build clusters of dots
Blocks and Puffs¶
- Show same technique as used in audio to create puffs of clouds
- Added blue background.
- Alpha and radial gradient background
- Adjust X and Y axis of gaussian to stretch the clouds into a more cloud-like shape
Not just puffs¶
Can also use these processes on colors.
- Use uniform distribution for picking colors randomly
- Explore constraining to a subset of colors
- Used this technique and more to generate real paintins
Summary: Think functionally¶
- Parametize everything
- Use numpy array functions as much as you can
- Can combine wave, array, math from the Python stdlib for audio synthesis
- Sound and art composition are extremely similar
- Experiment with Gaussian distributions
Tones + filters = sound effects
- Play with looping, itertools
Image sequences + Reportlab = flipbook PDFs
- Use strokes and not fills
Save image + sound sequences as videos
Image composition can respond to audio input