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Stream Eddies

Stream eddies is a granular sound regurgitator that uses a machine learning audio classifier to sort and select samples. Similarly to other granular effects like Clouds/Beads and streamStretch~, this process is intended to take an ongoing but non-uniform source signal and give it a somewhat denser consistency. Streameddies works by chopping the incoming sound into 5-second samples, running the classifier on each sample, and then semi-randomly choosing samples to play back based on the classification values it has received most so far. For example, if after 2 minutes of input the classifier has returned "trombone" more often than any other label, the sample that plays back most frequently will be the one that has been most trombone-like according to the algorithm.

Stream eddies delivers sound and offers control over a few parameters through a web interface. In the interface, click on a classifier label to cross it out, removing it from consideration when the program is selecting samples. Click it again to un-cross it. Keep in mind that this program is meant to audify, not to endorse, the extremely reductive and arbitrary system of categorization that machine listening imposes on sound.


First, set up the classifier (see "Run Locally" steps in the classifier readme):

$ git clone
$ cd MAX-Audio-Classifier
$ docker build -t max-audio-classifier .
$ docker run -it -p 5000:5000 max-audio-classifier

Then (in a new command line window), install stream eddies:

$ git clone
$ cd stream-eddies
$ npm install
$ node index.js

In your browser, go to localhost:4000


an experiment in classified audio regurgitation






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