It's no coincidence that many programs using artificial intelligence techniques are open source and thus completely free. This is because the early approaches originated in academia, where free licences for software are common practice in order to promote collaboration and further development. Here, however, it is not about frameworks and libraries for forms of AI, but about tangible and useful applications of artificial intelligence for your own computer. The term AI encompasses various methods such as neural networks, machine learning, deep learning, or natural language processing. In the following compilation, all these approaches are represented. The various approaches to pattern recognition, machine-processed decision trees, and automation of tasks are built on training data and models that are already ready. The availability of this data is one of the reasons why useful AI techniques are available in freely available software today at all.

Imagine being able to hear exactly what's under the hood of any piece of recorded music. You upload a file and a few minutes later, a song like "Born to Run" splits apart to reveal its secrets. Each player's mastery is laid bare: There's Bruce Springsteen's isolated vocal take, every murmur and cry heard clearly; Garry Tallent's propulsive bassline; Clarence Clemons' fired-up saxophone solo; and that memorable sprinkling of glockenspiel, courtesy of Danny Federici. Such is the promise of Spleeter, a free, open-source AI tool that was developed and released by the streaming service Deezer late last year. Using a process called source separation, Spleeter splits the audio file of any given song into four new audio "stems," which isolate particular instruments or groups of instruments: vocals, bass, drums, and so on.

The real power of machine learning may have nothing to with automating music making, and everything to do with making sound tools hear the way you do. While not a broadly known topic, the problem of source separation has interested a large community of music signal researchers for a couple of decades now. Wait a second – sure, you may not call it "source separation," but anyone who has tried to make remixes, or adapt a song for karaoke sing-alongs, or even just lost the separate tracks to a project has encountered and thought about this problem. You can hear the difference between the bassline and the singer – so why can't your computer process the sound the way you hear? Splitting stems out of a stereo audio feed also demonstrates that tools like EQ, filters, and multiband compressors are woefully inadequate to the task.

Cleanly isolating vocals from drums, bass, piano, and other musical accompaniment is the dream of every mashup artist, karaoke fan, and producer. Commercial solutions exist, but can be expensive and unreliable. Techniques like phase cancellation have very mixed results. The engineering team behind streaming music service Deezer just open-sourced Spleeter, their audio separation library built on Python and TensorFlow that uses machine learning to quickly and freely separate music into stems. The team at @Deezer just released #Spleeter, a Python music source separation library with state-of-the-art pre-trained models!

Splitting a song into separate vocals and instruments has always been a headache for producers, DJs, and anyone else who wants to play around with isolated audio. There are lots of ways to do it but the process can be time-consuming and the results often imperfect. A new open source AI tool makes this tricky task faster and easier. The software is called Spleeter and was developed by music streaming service Deezer for research purposes. Yesterday the company released it as an open source package, putting the code up on Github for anyone to download and use.

The silence was shredded by the rat-tat-tat eruptions of a single gun. More soldiers fired, their volleys coalescing into the grim music of war -- a sustained snare drum roll soon interrupted by the bass thumps of the 50-caliber machine gun. All the barrels pointed at a speck tracing a line in the sky over west Mosul. Their target was yet another drone dispatched by Islamic State. In the seven months of the Iraqi government's drive to recapture Mosul from the jihadists, small drones have become a signature tactic of the group: Their appearance on the horizon, loaded with a camera, signals that punishing mortar barrages will soon be on the way.