openmusic VS orange

Open Music has precompiled applications for Windows/86, Linux/86, macOS 86&ARM.

https://github.com/openmusic-project/openmusic/releases/tag/...

Other Common Lisp applications for music, written in LispWorks:

ScoreCloud, Music Notation: https://scorecloud.com

MusicEase, Music Notation: https://www.musicease.com/

OpenMusic, Music composition with a visual programming language: https://github.com/openmusic-project/openmusic/

OM#, based on OpenMusic: https://github.com/cac-t-u-s/om-sharp

Most of these applications are available for Mac and Windows, some even for Linux.

OpusModus (mentioned in the article) now is on Macs (Intel / Apple Silicon) and an upcoming version is promised for Windows: https://opusmodus.com

For example IRCAM (French institute dedicated to the research of music and sound ) develops graphical/visual programming systems for music composition: OpenMusic is a long time project, which is Open Source, but runs only on top of LispWorks, because of its GUI capabilities. A project derived from OpenMusic is OM#, which also written on top of LispWorks. ScoreCloud is a commercial product written with LispWorks. Also OpusModus (a commercial music composition system written in Clozure CL) is currently being ported to LispWorks (mentioned by the developers): then it will be possible to run on new Macs and also on Windows.

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.