NSynth Super is part of an ongoing experiment by Magenta: a research project within Google that explores how machine learning tools can help artists create art and music in new ways.
Rather than combining or blending the sounds, NSynth synthesizes an entirely new sound using the acoustic qualities of the original sounds—so you could get a sound that’s part flute and part sitar all at once.
Like all Magenta projects, NSynth Super is built using open source libraries, including TensorFlow and openFrameworks, to enable a wider community of artists, coders, and researchers to experiment with machine learning in their creative process.
The open source version of the NSynth Super prototype including all of the source code, schematics, and design templates are available for download on GitHub.
Magenta Studio is a collection of music plugins built on Magenta’s open source tools and models. They use cutting-edge machine learning techniques for music generation. There’s a bunch of Ableton Live pluginsyou can download for free.
Plus there’s a Neural Synth Instrument for Ableton or Max you can download as well.
This is the Dirtywave M8. It looks like a great piece of kit to play around with. I’ve stuck this up on Interesting in Sleevenotes so I remember about it further down the line when it can be bought in the UK and so that other interested people can see it too.
Anything that’s a synth + sampler + sequencer has my interest. Good work!
Portable tracker sequencer and synthesizer M8 is powered by the Teensy micro-controller and inspired with love from the renowned Gameboy tracker Little Sound DJ. Featuring 8 tracks of freely assignable instruments capable of a wide range of sounds including waveform synthesis, FM, virtual analog, sample playback, and MIDI output. Check out #M8Tracker on Twitter and our Discord Server meet-up videos on Youtube.
Iterations is a new way to help you generate ideas.
Creative methodologies usually combine divergent thinking (applying a stimulus to generate potential outcomes) and convergent thinking (bringing facts and data together and applying logic).
Iterations help divergent thinking by giving suggestions on how to process the output. They work as a set of guitar effect pedals to your process. There is an input, a set of ambiguous instructions that can be applied to this input, and an undetermined output created by these effects.
In layman’s terms: you come up with an idea, then click for a card. Then create a new idea based around your interpretation of what the card says. Then click for another card. Develop another new idea based on what the card says. And repeat. The results can lead iterative paths down new and innovative paths not normally reached through standard idea generation.
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