Ariana Grande, has added something new to her sell out stadium tours. She is controlling her vocals using gloves. Yep, gloves! To add reverb to her voice, Ariana pinches her thumb and forefinger. She changes background sounds by a sweep of the hand.
Imogen Heap, a Grammy award winning UK recording artist with a passion for technology, is behind the gesture control gloves that Florida born pop diva Ariana is wowing audiences across the world with.
Using technology to augment and change vocals is not new, sound engineers with banks of buttons and sliders have manipulated and improved performances for years, but now the artist can do it for themselves, using wearable tech with with bluetooth to control their sounds live.
So puff out your chest, robin and hear the humans notch up the sound gymnastics, we are not just limited to our vocal cords. Have a go at making wearables that control sound yourself. Maybe try Sonic Pi with a BBC micro:bit and search for the BBC’s ‘Strictly micro:bit live lesson’ for more on making your own wearable tech.
Google’s Fitbit is a smart wristwatch which doesn’t just tell you the time but can also monitor your movements and your heart beat. A particular time of day when your heart beat slows down and you move much less is at night when you’re fast asleep in bed.
Not everyone sleeps well though. Some people struggle to get to sleep and then wake up often during the night and so they feel tired during the day. The FitBit’s “Sleep Profiles” is an AI-supported sleep tracking tool (available to Premium subscribers) that may be able to help them. If the sleeper regularly wears their watch in bed it can monitor their sleep and build up a picture of how long it takes them to fall asleep, how often they wake up and offer some suggestions on how to get a better night’s rest.
So far Google has analysed 22 billion hours of sleep data from Fitbit users (who all agree to share their information so that they and everyone else can benefit from that shared knowledge). They used unsupervised machine learning to find out more about the data. This method gives an artificial intelligence lots of information but doesn’t tell it what to do with it. Instead they asked the AI to cluster groups of data together for the scientists to analyse and interpret. The result was six clusters of data showing the most common different ways that people sleep.
To make it easy for users to understand what the data meant, and how closely their own sleep pattern matched one of the clusters, Fitbit named each cluster after an animal. They took a bit of care over selecting animals to use as they wanted people to have more positive associations (no one wants to be called a sloth for example!) and came up with bear 🐻 tortoise 🐢dolphin 🐬giraffe 🦒parrot 🦜and hedgehog 🦔. People’s ‘sleep animals’ don’t stay the same though (just like our sleep) and you might be a dolphin one month and a tortoise the next. Tortoise-sleepers spend longer in bed but also take longer to fall asleep, and dolphin-sleepers sleep very lightly and tend to spend more time awake in bed.
Elena Perez, one of the product managers for Fitbit, said that parents of little children had told her that they’d seen the icon of the sleeping animal appear on their parents’ watch and knew that it was time to go to bed. Sweet dreams…
Did you know?
Dolphins and many birds use ‘unihemispheric sleep’ which means that one half of their brain (like humans their brains are also divided into two hemispheres) falls asleep first and the other stays awake. Then the hemispheres swap over!
In November, 2025, this catchy new country music song received lots of media attention. There’s nothing very unusual about that but what made this song unusual was that the whole thing (the words, the tune, even the singer) was created entirely by an artificial intelligence. There is no ‘Breaking Rust’, it’s all computer-generated. Now that you know that, does it make a difference to what you think of the song?
Lots of people are uneasy about a piece of music that had almost no direct human input into its creation. Music is a creative thing, designed and created by people and it feels unsettling to have computers doing that: for many it feels a bit like cheating. This song sounds human but if you listen carefully the singer seems to be performing the super-human feat of singing long stretches of the tune without taking a breath! A computer can do that, but people need oxygen!
And what is the future, if we are happy to listen to machine created things, that can be cheaply generated? Far less work, so livelihood, for human creatives. This is already happening in the world of the illustrator where it is harder than ever for newly graduated illustrators to get a foot on the ladder. Is that what we want for song writers and musicians too? Eventually, even the people running the programs to initiate the creation won’t be needed. If you want to listen to a new country song, or a new band, you will be able to click a button (pay some cash) and get one tailored for you. The money will go direct to a tech billionaire, of course.
Another thing people are very uneasy about is how the AI learned to write in that style of music in the first place. Music AI tools have been trained on vast amounts of other people’s music and, not surprisingly, many of those musicians are angry that their hard work has been re-used without permission or payment. Some musicians and music companies are now fighting back. They’ve asked lawyers to help them work with the AI companies so that they won’t lose out – they can instead opt in to allow their music be used to train AI tools, and this time they’ll be paid. This is basically what happens when musicians use the ideas of other musicians. Famously, “I’ll Be Missing You” by American rapper Puff Daddy and American singer Faith Evans, for example, used a sample without asking from the Police song, “Every Breath You Take”. Sting sued and as a result gets all the royalties from the song (though then had similar disputes with the other members of the Police!
A share of royalties might be a win for some of the musicians, and for the people who own the AI tools… but it still doesn’t solve how we might feel about AI music created by machines, or for future human musicians who might never get a break because new song writers can’t get a foot in the door. If you value people, you need to show it in what you watch, read and listen to!
Jo Brodie and Paul Curzon, Queen Mary University of London
The article above was inspired by BBC Tech Life’s 2nd December 2025 episode which had a 12min segment on AI-generated music. You can listen to the programme at https://www.bbc.co.uk/sounds/play/w3ct6zps [EXTERNAL]
Our new magazine, which you can also read online, on Music and AI
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Answers are at the bottom of https://cs4fn.blog/bitof6 where you can also read a copy of the magazine articles about Music and Artificial Intelligence.
Clues
1. _ _ _ _ _ a piece of text with musical symbols instead of letters that tells a performer which notes to play, also a piece of music that accompanies a film (5 letters)
2. and 10. _ _ _ _ _ _ (6 letters) separation is when computer scientists use AI to take a piece of music and split it into its _ _ _ _ _ (5 letters) – read more about this in ‘Separate your stems‘
3. The _ _ _ _ _ _ is the main part of the tune you might sing along to (6 letters)
4. A piece of music is made up of lots of different _ _ _ _ _ (5 letters)
5. We measure how loud something is in _ _ _ _ _ _ _ _ (8 letters)
6. A sequence of instructions that tell a computer what to do _ _ _ _ _ _ _ _ _ (9 letters)
7. If you halve the length of a guitar string the note is an _ _ _ _ _ _ (6 letters)
8. A guitar-like harp-lute from Ghana _ _ _ _ _ _ _ _ (8 letters) – read more about this in ‘The day the music didn’t die‘
9. How high or how low a musical note is _ _ _ _ _ (5 letters)
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Listen to a song and you might tap your feet. Computers can “listen” to music but they don’t have feet to tap! They don’t have ears or a brain either so they don’t “listen” in the way that you do. They use maths.
Turning sound into numbers
A computer is just a machine that does calculations on numbers. It doesn’t really “hear” music. To it everything is just numbers. Its programs convert sounds into numbers that it can do maths with.
When someone plucks a guitar, the string vibrates (wobbles back and forth). That sends a pulse of energy (a sound wave) through the air. Our ears detect that pulse. A computer measures the sound wave. A song has lots of different sound waves mixed together, and they can all be described with numbers that a computer measures.
One measurement is pitch – how high and squeaky or how low and rumbly the sound is. A guitar string playing a higher note vibrates faster than a lower note, sending its energy pulses into the air more quickly. We measure that as the number of sound waves arriving each second (called the frequency).
If we could see a sound wave it might look a bit like this. The red sound wave has a lower frequency than the blue sound wave where the distance between each ‘wobble’ narrows. Image by CS4FN
The red and blue wavy line shows what a sound wave might look like if we could see it. The blue part of the wave is vibrating faster than the red so has a higher frequency. Humans hear it as a higher note, computers ‘hear’ it by sensing more soundwaves each second.
Image by CS4FN
Another measurement is the volume, or how loud the sound is. That relates to how hard the guitarist plucked the string so how ‘tall’ the sound wave is. The wavy black line has the same frequency as the red and blue wave but the black sound wave is bigger: it has a larger amplitude. Humans hear it as louder, computers record bigger numbers.
Once a computer has recorded the measurements as numbers, it can then do maths on the numbers. That is where things get interesting. Programs can then change the numbers to make new and different sounds. Or they can use algorithms to generate their own numbers, then play them as music!
How loud?
Volume is measured in decibels (dB for short). A lower number means the sound is quieter, a higher number means it is louder. The loudest a UK car is allowed to be is 70 dB.
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There are infinitely many musical notes, just like there are infinitely many colours. That matters if you are designing a new digital musical instrument. You have a lot more choice than on a piano!
Octaves
Most Western music is divided equally into groups of 12 notes (‘octaves’) that musicians use. The gap between any two notes sounds the same. This is known as equal temperament tuning.
Activity:Play the 12 notes
You can play the 12 notes of an octave on the online piano https://bit.ly/pianoCS4FN. Play Middle C (marked with a red dot), then press each key in turn including the black keys. Play 12 notes and you have played the 12 notes of an octave.
Music as colour
The rainbow picture (below) shows there are many colours to pick from not just red, orange, yellow… A set of crayons would be enormous if it included every possible colour! Instead you get a selection just as in the picture: we picked 3 colours equally spaced apart: red, yellow and blue. Western music does the same thing with sound, picking 12 notes that sound equally spaced.
Image by CS4FN
There are lots of other notes that you could sing within an octave. Traditional music often uses different sets of notes. The Arabic system divides an octave into 24 notes, for example. They have more ‘sound crayons’ to play with! You could even start singing on a low note and continually raise your pitch until you reached the higher note, like sweeping through every colour in a musical rainbow.
If you sing a note, then sing the same note but an octave higher (eg Middle C then the next highest C), your vocal cords are now vibrating twice as fast! The frequency of the top note is twice as high as the lower one. Your vocal cords doubled their speed.
Jo Brodie and Paul Curzon, Queen Mary University of London
More on…
We have LOTS of articles about music, audio and computer science. Have a look in these themed portals for more:
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How can a machine generate music? It needs an algorithm to follow: instructions to tell it what to do, step by step. Here are two simple games to play that compose a random tune by algorithm.
Writing Notes
We need a way to write notes. We use letters A to G as on a piano. They repeat all the way up the white keys, so after G comes different higher versions of A, B, C again. We will use notes running from what is called Middle C in the middle of the piano to the next C up. This is called an octave. We will call the two Cs, C1 and C2.
Game 1: Random Jumps
Roll two dice and add the numbers. Write down the note given in the table for Game 1, so if they add to 2 or 3 write down C1, if 4 write down D…If 7 then you get to roll again, and so on. Keep going until you have written 15 notes to make a tune of 15 notes.
Game 1 by CS4FN
Game 2: Up and Down
The second algorithm uses one die. First write down C1 then roll the die and do what it says in the Game 2 table. Each new note is based on the last note. If you roll a 1 then write down D (the next note UP from C1). Rolling a 6 means add a pause in the tune (write a dash). If the roll takes you beyond either C then you bounce back: so rolling a 4 when you last wrote C1 means you write C1 again. Rolling 5 from C1 bounces you up to E. Continue until you have 15 notes.
Are they any good? Does either game give better tunes?
Good music isn’t just random notes. That is why we pay composers to come up with the really good stuff! Both human and machine composers learn more complicated patterns of what makes good music.
What do you think of our musical masterpiece?
On Game 1 we rolled 6 4 8 8 8 | 5 9 4 9 6 | 5 6 9 9 10 so our tune is F D G G G | E A D A F | E F A A B
Here’s what our tune sounds like once harmonies have been added.
Could you improve your tunes by tweaking the notes? Some people use simple algorithms to spark human creativity like that. Rock legend David Bowie helped write a program he then used to write songs. It took random sentences from different places, split them in half and swapped the parts over to give him ideas for interesting lyrics. It was possibly the first algorithm to help write hit songs.
A ‘note’ on bias
Think about the numbers that are rolled and the number of different ways that each number can be produced. For example with two dice (let’s call them ‘left’ and ‘right’) you can make the number 9 twice by rolling a 5 with the left and 4 with the right, or 4 with the left and 5 with the right. Same with 6 and 3. There are only two ways to roll a 2 (both dice have to show 1) or a 3 (a 1 and a 2 or a 2 and a 1). This is baked in to the process and so will affect the notes that appear most often.
Jo Brodie and Paul Curzon, Queen Mary University of London
More on…
We have LOTS of articles about music, audio and computer science. Have a look in these themed portals for more:
The Music and AI pages are sponsored by the EPSRC (UKRI3024: DA EPSRC university doctoral landscape award additional funding 2025 – Queen Mary University of London).
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Computer Scientists are working to support traditional music from around the world.
A seperewa is a traditional “harp-lute” musical instrument of the Akan people in Ghana, Africa. It has strings that are plucked a bit like a guitar. It is dying out because of the rise of western music. Researchers are now testing AIs that were trained on western music to see if they still work with such different seperewa music. They are also trying to understand exactly how this traditional music is different.
Protecting traditional instruments
Colonisers introduced European guitars to Ghana in the late 1800s and their sound began to influence and even replace seperewa music. Worried by this, in the mid-1900s people made recordings to preserve endangered seperewa music and to remind people what it sounds like. Ghanaian musicians are now reviving the seperewa, so we might continue to hear more of its lovely sound in future.
A team of computer scientists and music experts have investigated recordings of seperewa music to see how well western AI tools can analyse that style of music, given it is tuned in a completely different way, so plays different notes to a western instrument.
First the team used one AI tool to separate the sounds of the seperewa from the singing. It struggled a bit and left some of the singing in the seperewa track and vice versa but overall did a good job,
They then used a different AI to analyse the sounds of the seperewa. The found that the seperewa music had its own, unique musical fingerprint, revealing a rich tapestry of sound that was clearly different from western music.
The research is helping to preserve a vital part of Ghanaian culture. It has shown in detail how their music is different to anything western and so that something unique and precious would be lost if it died out.
Jo Brodie and Paul Curzon, Queen Mary University of London
The Music and AI pages are sponsored by the EPSRC (UKRI3024: DA EPSRC university doctoral landscape award additional funding 2025 – Queen Mary University of London).
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600 years ago King Sejong the Great of Korea published ‘Hangul’, a new and improved writing system for his people. To celebrate he asked his court scholars to write an epic poem in Hangul, then asked his musicians to compose music to accompany it. The result was Yongbieocheonga, or ‘Songs of the Dragon Flying to Heaven’.
It was performed by musicians playing wind and stringed instruments. The musical instruments the AI composed for are Daegeum and Piri (wind instruments), Haegeum and Ajaeng (bowed string instruments) and Geomungo and Gayageum (plucked string instruments). Each instrument had its own melody written out for the musician to follow. Only one piece of the written music survives fully intact (it is still performed!). Melodies of other pieces of music have survived but only for a single instrument. That means those pieces can’t be played by a group of musicians because all the other harmonies are missing.
A team of computer scientists decided to recreate the missing 15th century Korean harmonies from just the single melodies (in the way the Bach Google Doodle does, see You’ll Be Bach!). They wanted to expand the ability of their AI tools to make sense of music beyond western music.
They first taught their AI musician to recognise Korean music written in Hangul. Then, it learnt which notes sound best played together by different instruments. Finally, to generate music that could be played, it matched melodies and rhythms.
It created a melody for each different instrument. The researchers then asked Korean musicians to perform the whole piece and to judge how well the AI musician had done. Happily, they thought that the music worked well and sounded correct. They could perform it with only a few small tweaks.
You can listen to one of the performances and find out more below.
Jo Brodie and Paul Curzon, Queen Mary University of London
Watch…
Hear what a piri sounds like in this short YouTube video (in Korean with English subtitles) [EXTERNAL]
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Purple can be created by mixing together red and blue paint. You can probably tell which of the faces in the image has more blue and which has more red. Does music work the same way?
Your brain can recognise the red and blue in purple while still seeing it as a whole colour. Music is similar. When you listen to a song your ears and brain hear all the sounds at once. The singing, guitars, drums and keyboard parts are mixed together, but you can also focus on the singing, or the keyboards or ….
Computer scientists have gone a step further with Artificial Intelligence. By training AI tools on lots of different songs they have taught them to do “source separation” – unmixing a recorded song back into its separate bits. Those separate bits are called stems. It is like taking purple paint and unmixing it to give blue and red again!
Photographer Todd McLellan photographs gadgets he’s carefully taken apart, to show all the pieces (search the web for his “Things Come Apart”). When a piece of music is blended together and an AI separates it again it’s a bit more like trying to un-bake a cake!
Jo Brodie and Paul Curzon, Queen Mary University of London
More on…
We have LOTS of articles about music, audio and computer science. Have a look in these themed portals for more:
The Music and AI pages are sponsored by the EPSRC (UKRI3024: DA EPSRC university doctoral landscape award additional funding 2025 – Queen Mary University of London).
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