Research in music and AI has never been so prolific nor so exciting. However, a large majority of research is kept in university laboratories, with little connection to the outside world. I decided to have a deeper look to see what current AI jazz improvisation programs or applications exist which could help teachers and practitioners develop improvisational skills in their students and themselves. AI is still seen as something mysterious within music circles, so I hope as time goes by, to dispel some of this and to inform musicians/teachers on current AI realities. Indeed, it is becoming increasingly important for the wider world to become involved in discussions around these technologies, and to ensure that AI develops in directions that will serve people, rather than dominate or eliminate them.

In particular, so-called 1/fα noise series with α close to 1 (also called pink noise) occur in sound, music and countless human artifacts or natural events, from the fluctuations of the flood levels of the Nile to movements of the stock market. As a consequence, many generative models for 1/f noise have been designed to produce series that look or sound "natural" or "human". In this paper, we formulate the generation of 1/f series as a hard constraint satisfaction problem, so that 1/f noise generation can be used as an add-on to arbitrary sequence generation problems. We take inspiration from a simple yet beautiful stochastic algorithm invented by Voss and introduce the Voss constraint. We show that Voss' algorithm can be modeled as a tree of ternary sum constraints, leading to efficient filtering. We illustrate our constraint with a melody generation problem, and show that the addition of the Voss constraint tends indeed to produce sequences whose spectrum have a 1/f distribution, regardless of the other constraints of the problem. We discuss the advantages and limitations of this approach and possible extensions.

This work-in-progress paper describes attempted improvements on Pachet's Description-Based Design (DBD), a system that uses machine learning to generate melodies. We discuss in depth both Description-Based Design and our extensions to Pachet's original approach. We also present a user study in which users had some success in transforming melodies and describe the implications of these results for future work.

Songwriters of global hits getting sued for alleged plagiarism has become a recurrent story on MBW these past few years – and a recurrent source of misery for writers and their representatives in the industry. But what if a songwriter or composer were able to use AI technology to avoid litigation altogether, by finding out if their song copies elements of other compositions, potentially in real time? That could now be a reality, thanks to a Spotify invention revealed in a new European patent filing from the company obtained by MBW. According to a document published last week, Daniel Ek's company is seeking a patent for its "Plagiarism Risk Detector And Interface" technology, which pertains to "Methods, systems and computer program products..for testing a lead sheet for plagiarism". As explained in the filing – and as our songwriter/musician readers will already know – a'lead sheet' is a type of music score or musical notation for songs denoting their melody, chords and sometimes lyrics or additional notes.

The use of artificial intelligence is set to revolutionize how people create music but still, robots will not replace humans in the art of making melodies, attendees of a conference about art and music were told on Sunday. "Artificial intelligence will not replace good artists and composers," François Pachet, a scientist, composer and the director of the Spotify Creator Technology Research Lab, told participants of the TechnoArt 2019 conference in Tel Aviv. "AI will change the way people make art, but it won't replace them." Pachet is considered a pioneer of computer music, and specifically its interaction with AI. At Spotify he leads development of AI-based tools for musicians.

The first testing sessions for SampleRNN – an artificially intelligent software developed by computer scientist duo CJ Carr and Zach Zukowski, AKA Dadabots – sounded more like a screamo gig than a machine-learning experiment. Carr and Zukowski hoped their program could generate full-length black metal and math rock albums by feeding it small chunks of sound. The first trial consisted of encoding and entering in a few Nirvana a cappellas. "When it produced its first output," Carr tells me over email, "I was expecting to hear silence or noise because of an error we made, or else some semblance of singing. The first thing it did was scream about Jesus. We looked at each other like, 'What the fuck?'"

Pop music made by actual robots is here… and it sounds considerably better than you might think. Hello World, released earlier this month via Flow Records, is being touted as'the first multi-artist commercial album created using Artificial Intelligence'. The LP has been recorded by French collective SKYGGE, in collaboration with the likes of Canadian chart-topper Kiesza and Belgian pop star Stromae… and, of course, those all important computers. SKYGGE is led by composer, author and producer Benoit Carré, alongside a gentlemen who is becoming increasingly well-known (and slightly fretted about?) in music business circles: François Pachet. Pachet (pictured) is the world's foremost scientist in the field of AI-assisted music creation.

The first full-length mainstream music album co-written with the help of artificial intelligence (AI) was released on 12 January and experts believe that the science behind it could lead to a whole new style of music composition. Popular music has always been fertile ground for technological innovation. From the electric guitar to the studio desk, laptops and the wah-wah pedal, music has the ability to absorb new inventions with ease. Now, the release of Hello World, the first entire studio album co-created by artists and AI could mark a watershed in music composition. Stemming from the FlowMachines project, funded by the EU's European Research Council, the album is the fruits of the labour of 15 artists, music producer Benoit Carré, aka Skygge, and creative software designed by computer scientist and AI expert François Pachet.

François Pachet has the capacity to change the music industry as we know it. You probably don't know who he is. I don't expect you to. He's a French scientist and foremost expert on music composed by artificial intelligence. Yes, music composed by computers.

From Elgar to Adele, and the Beatles or Pink Floyd to Kanye West, London's Abbey Road Studios has hosted a storied list of musical stars since opening in 1931. The man sitting at the keyboard where John Lennon may have finessed A Day in the Life is Siavash Mahdavi, CEO of AI Music, a British tech startup exploring the intersection of artificial intelligence and music. His company is one of two AI firms currently taking part in Abbey Road Red, a startup incubator run by the studios that aims to forge links between new tech companies and the music industry. It's not alone: Los Angeles-based startup accelerator Techstars Music, part-funded by major labels Sony Music and Warner Music Group, included two AI startups in its programme earlier this year: Amper Music and Popgun. This is definitely a burgeoning sector.