It’s not quite like adjusting randomness – hence they chose this new name – but it will give you some control over how predictable or unpredictable the results will be (if you also accept that the relationship may not be entirely linear). There’s also a slide called “Temperature” which determines how the model is sampled mathematically. You’ll make new clips – sometimes starting from existing clips you input – and the device will spit out the results as MIDI you can use to control instruments and drum racks. Magenta Studio lets you work with MIDI data, right in your Ableton Live Session View. And now instead of just watching a YouTube demo video or song snippet example, you can play with the tools interactively. But it’s a lot better to dive in and see what the results are like first. If you want to read a summary of that MusicVAE research, you can. Machine learning lets you work from large sets of data, and then not only make a model, but morph between patterns and even generate new ones – which is why this gets interesting for music software.Ĭrucially, you don’t have to understand or even much care about the math and analysis going on here – expert mathematicians and amateur musicians alike can hear and judge the results. Music theorists have looked at melodic and rhythmic transformations for a long time, and very often use mathematical models to make more sophisticated descriptions of how these function. Many are based on MusicVAE – a recent research model that looked at how machine learning could be applied to how different melodies relate to one another. Magenta Studio has a few different tools. (That requires a little more knowledge and some time for your computer or a server to churn away, but it also means you shouldn’t judge Magenta Studio on these initial results alone.) One reason that it’s cool that Magenta and Magenta Studio are open source is, you’re totally free to dig in and train your own data sets. Build a model based on a data set of bluegrass melodies, for instance, and you’ll have different outputs from the model than if you started with Gregorian plainchant or Indonesian gamelan. That means it needs a set of data to “train” on – and part of the results you get are based on that training set. Magenta’s “musical” library applies a set of learning principles to musical note data. We say it’s “learning” in the sense that there are some parallels to very low-level conceptions of how neurons work in biology, but this is on a more basic level – running the algorithm repeatedly means that you can predict sequences more and more effectively given a particular data set. Recurrent Neural Networks are a kind of mathematical model that algorithmically loops over and over. Some of the grittier artifacts produced by this process even proved desirable to some users, as they’re something a bit unique – and you can again play around with in Ableton Live.īut even if that particular application didn’t impress you – trying to find new instrument timbres – the note/rhythm-based ideas make this effort worth a new look. NSynth uses models to map sounds to other sounds and interpolate between them – it actually applies the techniques we’ll see in this case (for notes/rhythms) to audio itself. That also has its own Ableton Live device, from a couple years back. You may know Magenta from its involvement in the NSynth synthesizer. It takes the stuff you’ve been doing in music software with tools like grids, and lets you use a mathematical model that’s more sophisticated – and that gives you different results you can hear. Seeing the results of this machine learning in action means having a different way of generating and modifying musical information. But it’s really about creating an engine that can very quickly process lots of tensors – geometric units that can be combined into, for example, artificial neural networks. “TensorFlow” may sound like some kind of stress exercise ball you keep at your desk. What Magenta and TensorFlow are based on is applying algorithmic analysis to large volumes of data. AI?Īrtificial Intelligence – well, apologies, I could have fit the letters “ML” into the headline above but no one would know what I was talking about. But let’s back up and first talk about what this means. I got to sit down with the developers in LA, and also have been playing with the latest builds of Magenta Studio. And, if you’re a developer, you can dig far deeper into the tools and modify them for your own purposes – and even if you have just a little comfort with the command line, you can also train your own models. Because they’re built with Electron (a popular cross-platform JavaScript tool), though, there’s also a standalone version. If you’re working with Ableton Live, you can use Magenta Studio as a set of devices. After some more polishing, Magenta Studio is now ready for primetime use since its full release earlier this year.
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