The first try probably won’t be very good, but computers work fast, and each failure gives them more to learn from for their next try. Then the computer takes everything it’s learned about those songs and tries to create something new that fits the same profile. But by studying thousands and thousands of different songs, machine learning allows them to create a profile of all the different elements that those songs have in common, much the same way that facial recognition programs use thousands of pictures of people’s faces to teach them what to look for. There’s a link to the song below, and I think you’ll agree that the result are uncanny:īut how did we come all the way from a computer beeping the national anthem to writing and singing its own songs? What does machine learning really mean, and what does it mean for the future of the audio industry? The answer lies in patterns, and a computer’s ability to recognize patterns and then generate new ones.Ĭomputers don’t really know what music means, or even that they’re making music.
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In 2019 Yamaha used its new VOCALOID:AI vocal software to recreate the voice of legendary singer Hibari Misora on the 30th anniversary of her passing. They can even mimic human voices, using deep learning paired with existing recordings to study and then duplicate a particular kind of voice. Here’s a link to “Mister Shadow,” a song entirely composed and performed by Sony Computer Science Lab’s “Flow Machines” AI system: Now, thanks to machine learning and the development of artificial neural networks, computers can compose their own songs with hardly any human guidance at all.
Of course, synthetic music’s come a long way over the past seventy years. Want to hear the Ferranti’s groundbreaking music for yourself? Just check out the link below for a digitally restored recording of that historic moment, and what the people listening had to say: This early melding of art and science would pave the way for similar fusions of musical and scientific genius over the years.
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It’s a sample of three songs created by Alan Turing’s Ferranti Mark 1 computer, which filled up a whole room the melodies were programmed by Christopher Strachey, a computer scientist who also drew upon his experience as a piano player to teach the computer how to play music.
There’s some debate on just when the first electronic music was created, but the oldest recording comes from 1951. This sort of musical collaboration between humans and computers has been evolving for a surprisingly long time. Just recently Google released Blob Opera, a machine learning tool by David Li that “pays tribute to and explores the original musical instrument: the voice.” There’s a link below for you to try it out for yourself: all you have to do is direct each of the singing blobs by sliding its range up and down with your mouse, and listen as they compose their own harmonies. One of the most innovative uses for machine learning, however, is in creating music. Machine learning has helped shape just about every aspect of our digital lives, whether it’s deciding which Netflix show or YouTube video to recommend to us or even teaching cars to drive themselves.
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