This algorithmic piece picks random notes from the Bohlen-Pierce scale for a soothing ambient effect. There are 17 iterations, each dropping the highest note from the list. It was composed in Java using the JMusic library.
Tag Archives: algorithmic
Overlapping Bohlen Pierce chords. Each note includes a harmony 439 cents (9/7) above and another 439 cents below. A pleasant effect with no real parallel in the 12-tone system.
A longer version of the algorithm sheds high notes as it progresses:
This AV art project is called Tempus Fugit. The audio hasn’t changed, but in this 2nd version the video is more centered on a clock theme. Details below.
Playing with random elements is one of my favorite pastimes. I like the Bohlen-Pierce musical scale. I also like the way that sounds get out of phase when identical parts are played slower or faster. This audio-visual collage dubbed “Tempus Fugit” was built from 3 original sources, all of which were stretched or condensed to fit the allotted time of 4:58. It was constructed in Audacity on an iMac computer.
The strange sounds on the right came from a 1972 cassette microphone feedback experiment. Stumbling onto that cassette was the impetus of this project. Three copies were blended: the original, one pitched 1702 cents higher, and one pitched 1702 cents lower. 1702 cents is at octave plus a 5th – the size of a tritave in the Bohlen-Pierce scale. Blending the 3 gave the cassette sounds a much wider sonic range.
The chiming sounds in the center are random Bohlen-Pierce notes generated by Ruby code in the Sonic Pi Mac app. It was recorded in 2015. Two copies were blended, one a bit slower than the other. As one fades out over the length of the piece, the other fades in.
The drums on the left were played by Oscar Calderon as part of a shorter project in 2006. Two stereo copies were stretched into place, one a bit slower than the other.
The video was created by morphing images from the BigGAN library with deep-music-visualizer, a Python program by msiegs found on Github. Rendering the video took about 9 hours on the iMac. Here are the parameters:
409: ‘analog clock’,
826: ‘stopwatch, stop watch’,
892: ‘wall clock’,
Go for it:
python3 visualize.py --song TempusFugit.wav --classes 409 971 835 417 826 892 --num_classes 6 --sort_classes_by_power 1 --tempo_sensitivity 0.4 --depth 0.75 --smooth_factor 25 --jitter 1.0 --output_file TempusFugit2.mp4
Rendering time for the 5-minute video at 512 pixel resolution was about 9 hours. There are a few glitches – I don’t know what causes that.