Category: Artificial Intelligence

Emotional glasses

Image by Km Nazrul Islam from Pixabay

It’s fun to add emoticons to messages, and they help ensure people understand our feelings. They are helping some people understand feelings face-to-face too, with a bit of help from an Artificial Intelligence.

Reading faces

We take it for granted that we can look at someone’s face and tell whether they are happy or sad, angry or surprised. Autistic children, however, often struggle to understand people’s expressions. When anxious we also all tend to avoid eye contact. Some autistic children do that all the time. They are then even less likely to see the clues in people’s faces, and so start to understand emotions. This can make it harder to make friends.

From robots to glasses

Many hi-tech ways have been tried to help autistic children learn about emotions. One, for example, involves letting them play with robot ‘friends’ as some find the cartoon-like expressions on a robot face more comfortable and easier to follow. A different approach is based on wearable technology. Researchers at Stanford University have created a program for autistic children that works out a person’s expression and displays an emoticon of it in a pair of smart glasses.

An AI reading faces for you

A camera in the glasses records what the wearer sees and the Artificial Intelligence (AI) program detects any faces. This kind of technology is also used in smartphones to detect faces in your photo collection. It uses ‘machine learning’: the program learns what a face is by being shown lots of images, some with and some without faces. The program uses all that data to work out the patterns in an image that mean there is a face. It then uses that pattern to spot new faces.

In a similar way it can be trained on faces with different expressions. A training set of faces are used that are labelled with the emotion in that image. This allows the program to spot what pattern in a face makes a happy face, what makes a sad face, and so on. Having recognised an expression, the glasses finally act as a screen and show an emoticon, such as a smiley, corresponding to that expression. Superimposing digital images on the real world like this is called augmented reality. It makes looking at faces like a game and means that the child can use the emoticon to understand what the person in front of them is feeling. It also means they can start to learn for themselves – almost like the AI! The AI is labelling the faces for them, just as people had done for it. With the glasses, autistic children can be sure what each face is actually saying rather than having to guess. Eventually they might then form their own rules and so do it on their own.

Making a difference

The Stanford system was trialled with autistic children in their own homes. They used the system for several months and their parents found it made a clear difference. By the end many of the children were engaging much more with their family including making a lot more eye contact.

Emoticons are making a real difference to their lives.

– Paul Curzon, Queen Mary University of London

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How do you sleep? (Like a parrot or a tortoise?) #Fitbit

A brown bear sleeps, resting its head on its paw.
Image by Pixamio from Pixabay

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!

Jo Brodie, Queen Mary University of London

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The Hidden Code in Toy Adverts

A boy in blue and girl in pink playing on a beach
Image by Ben Kerckx from Pixabay

The music in a toy commercial isn’t just background noise. It tells you who the advert is for, and a machine learning model can hear it (even when you barely notice the difference). Luca Marinelli tells us more.

Next time you’re watching TV, try muting the adverts and then turning the sound back on. You’ll probably notice something odd. The music in adverts for dolls and playsets sounds completely different from the music in adverts for action figures and toy cars. One sounds smooth and tuneful. The other sounds loud and chaotic. But here’s the question: is that just your imagination, or is the difference real and measurable? For my PhD research at Queen Mary University of London I decided to find out using machine learning.

I collected over 600 toy commercials from a UK retailer’s YouTube channel, split into three groups: ads aimed at girls, ads aimed at boys, and ads aimed at mixed audiences. Then I fed the soundtracks into a computer program and had it extract dozens of measurements from each one. Not “does this sound nice?” (computers can’t answer that) but more precise numerical values like “how rough does the sound spectrum look”, “how regular is the beat” or “how clearly does this audio sit in a musical key”. Think of it as turning every piece of music into a long list of numbers that each describe a property of it.

Then I trained a type of machine learning model called a classifier, to look at those numbers and predict: is this intended as a girls’ ad, a boys’ ad, or a mixed one? The classifier got it right a remarkable 91% of the time when comparing girls-only and boys-only ads. That’s not luck. That’s a genuine, detectable pattern hidden in the sound. But which measurements were actually doing the work? This is where the research gets interesting, and where a technique called SHAP (Shapley Additive exPlanations) comes in. SHAP is a way of asking a machine learning model to explain its own decisions. Instead of just getting a yes/no answer, you can ask: “which features pushed you towards saying this was a girls’ ad, and which ones pushed you the other way?” It’s a bit like asking a judge not just for a verdict, but for their full reasoning.

What SHAP revealed was striking. Ads targeting girls consistently had higher harmonicity, meaning the sounds fit together into clear, pleasant musical patterns, and more rhythmic regularity, meaning the beat was steady and predictable. Their audio spectrum (a kind of fingerprint of all the frequencies present) was also broader and smoother. Boys’ ads, by contrast, scored higher on spectral roughness (sounds that are abrasive) and spectral entropy (a measure of how chaotic or unpredictable the sound is). They were also simply louder. In plain terms: girls’ ads sound harmonious and organised. Boys’ ads sound noisy, aggressive, and jagged. And a machine learning model can tell the difference with 91% accuracy just from the audio alone, without seeing a single frame of video. These patterns almost certainly aren’t accidental. Marketers are making deliberate choices about music to signal who a product is “for”. The sound itself carries a hidden message.

We showed how AI can be used to hold up a mirror to human behaviour. When we use explainable AI we can spot patterns in the world that are so familiar we’ve stopped noticing them. The music in a toy advert might seem trivial, but if an algorithm can reliably predict the intended audience just from the soundtrack, that tells us something important: gender stereotypes aren’t just visible, they’re audible too.

Luca Marinelli, Queen Mary University of London

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Humanity’s Last Exam

A robot sitting at a desk doing an exam
Image by Mohamed Hassan from Pixabay

Generative Artificial Intelligences (GenAI) can now pass exams we set for humans and even do better than many humans. They can do that even without being able to think in a way a human does, and certainly without being conscious. They are learning to reason and are combining that with having hoovered up all the knowledge we have generated and recorded whether on the web or elsewhere. In effect, they use it to predict what comes next. In an exam what comes next after a question is the answer, so that is what they generate. But how good are they at doing that, really? As good as a good school student? As good as a university student? A PhD student? A Professor? Better than any human? Is there any question we could come up with, as examiners representing the human race, that a GenAI couldn’t answer? The SafeAI Benchmark Competition “Humanity’s Last Exam” is an attempt to find out.

Computer systems including AI-based ones, are typically evaluated based on benchmark questions that assess their intelligence and performance. They are the equivalent of big standardised exams. However, as AI models have rapidly advanced, existing benchmarks have become too easy. The “Humanity’s Last Exam” competition aimed to change this by collecting a new benchmark set of exceptionally difficult questions. The aim was to push artificial intelligence to its limits by challenging it with truly expert-level questions. To stack the deck in our favour any AI aiming to pass needed to be an expert in every subject, not just one or two!

Experts from across the disciplines were challenged to come up with questions in their area that they thought an AI would not be able to answer. The competition was a big success. It attracted more than 1,000 researchers and other experts. They submitted questions (with the correct answers), spanning over 100 different subjects. From all these suggested questions a solid set were selected in three stages. 

First, came AI Evaluation: five of the best AI models of late 2024 attempted each question. If all failed it, then the question advanced to the next stage. Second came Expert Review: human experts refined and assessed the questions and answers. They had to make sure that the questions had a known answer that they were sure was correct. The questions also had to be clear. They couldn’t be ambiguous so that more than one answer might be considered correct. Finally, came the Final Selection: a panel of experts and organisers made the final call of which questions were actually to be used.

Out of over 70,000 submitted questions to stage 1, only 2,500 made it into the final benchmark, with the top 50 declared as winners, with the person submitting the question earning a prize. In addition, they were invited to become co-authors of the research paper accompanying the competition.

Two computer scientists from QMUL, Søren Riis and Marc Roth contributed multiple questions to the competition, and despite how many questions failed to make the grade, both were joint winners. Moreover, one of Marc’s questions was selected to be featured in the Nature paper about the results. 

But what does a good question look like? To see, lets look at one of Marc’s selected questions. It concerned the process of “discovering” a network, meaning visiting all the nodes of an unknown network. What does this involve? Imagine a mouse is placed in a maze and starts to explore it. The maze is a kind of network with nodes (the junctions) and edges (the paths between them). The mouse, as it explores, is discovering that network. Suppose it does it randomly. Whenever it reaches a junction, it chooses one of the outgoing directions totally at random and continues exploring in that direction. We are interested in several things: how long will it take a mouse, on average, to explore the entire maze? How often will any specific location be visited by the mouse? And how likely it is for the mouse to be at any specific location at the end of its exploration?

The AIs were asked about a variation of this in which the mouse uses a specific but cleverer random strategy as given in the question, rather than just choosing a direction to go in totally at random at each junction. The AIs had to predict the behaviour of a mouse following this new strategy on different types of mazes. Surprisingly perhaps, even the best AIs at the time of the competition (2024) were unable to solve the problem correctly. They all claimed that the updated strategy does not lead to any difference in the overall behaviour compared to the original naive random strategy, in terms of the things of interest (like time taken). This is wrong as there are actually clear differences in the behaviour resulting  from the two strategies. That was something that Marc himself was able to correctly work out: Humans: 1 (well at least if you are Marc), AIs: 0

The first version of the overall benchmark (so AI exam) was set and finalised in early 2025. The best two AIs (Open AI o1 and Deepseek R1) got about 8% of the questions right. One year later, Gemini 3 Pro achieved a staggering 38.3%! Its true performance might be even better since the benchmark set might still contain some ambiguous questions with no clear right answer and some questions where the given expert answers are only partially incomplete or incorrect. This is mainly believed to be a possibility in the areas of text-only chemistry and biology questions: so more work for the chemists and biologists!

Because of the need to continue to work on the questions to make sure they are definitely correct and unambiguous, the “Humanities Last Exam” team has now switched to working on the questions on a rolling basis, aiming to improve the questions over the coming years. The AIs are not going to be free from taking exams for some time come! But it may not be long before humanity runs out of questions. In the meantime, anyone thinking that human examiners need to just come up with better questions to avoid the problem of students asking AIs to answer questions for them had better think again. Even the best experts in the world are struggling to find questions no AI can answer. And if they can’t answer them this year, there is always next year, or the year after…

Marc Roth and Paul Curzon, Queen Mary University of London

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No pause for breath

A robot playing a keyboard
AI generated Image by Gerd Altmann from Pixabay

Before you read the article you should have a listen to this piece of music: “Walk My Walk” by Breaking Rust EXTERNAL, YouTube

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

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Music AI Kriss Kross Puzzle

A Kriss Kross Puzzle Puzzle design credit: https://puzzlemaker.discoveryeducation.com/criss-cross/

Puzzle design credit: https://puzzlemaker.discoveryeducation.com/criss-cross/

Download and print the puzzle

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)
  • 10. (see 2.)

Jo Brodie, Queen Mary University of London

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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 machines “hear” music

Bee playing a horn into a microphone
Bee image by by Clker-Free-Vector-Images from Pixabay

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).

A wave that starts red then become blue as the waves squash together
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.

A wave that starts red then become blue as the waves squash together. A black wave matches it exactly aside from being taller.
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.

How loud do you think these sounds are?

Table with volumes How Loud? Sound Volume Car 70dB Doorbell ? Jet plane taking off ? Breathing ? Vacuum cleaner ? Balloon Popping ? Whispering ? Rainfall ? A robin singing ? Loudest shout ever by a teacher ?

Answers at https://cs4fn.blog/bitof6/

Jo Brodie and Paul Curzon, Queen Mary University of London

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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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All the notes?

A boy with headphones surrounded by swirling music
Boy listening to music image by Olena from Pixabay

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.

A spectrum of colour running from red to blue with red, yellow and blue selected equal distances apart
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

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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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Musical Algorithms

An octave on a piano marked as from C to the next C labelled as C1 and C2
Image (edited) by OpenClipart-Vectors from Pixabay

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.

Table for Game 1 showing dice rolls and notes 2 or 3 - C1 4 - D 5 - E 6 - F 7 - Roll again 8 - G 9 - A 10 - B 11 or 12 - C2
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.

Table for Game 2 showing die rolls and action 1 - UP 1 note 2 - UP 2 notes 3 REPEAT note 4 - DOWN 1 note 5 - DOWN 2 notes 6 - PAUSE
Game 2 by CS4FN

Play your tunes

Play your tunes on any instrument or use a free online piano (see https://bit.ly/pianoCS4FN).

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

Make your tunes special!

See how on the Bach Google Doodle page.

A cloud of stars
Starburst by CS4FN

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

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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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The day the music didn’t die

An AI generated image of a skeleton wearing a red, blue and yellow sombrero hat, sitting on a stool while playing a guitar against a colourful red and yellow background.
Image by AiRebellion from Pixabay

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 view of a historical seperewa instrument side-on showing a large sounding box with strings attached to a neck, and stretched taut for playing.
A seperewa, adapted from a public domain image on Wikipedia.

AI to the rescue

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

Watch …

Hear what a seperewa / seprewa sounds like at this YouTube video: The seprewa – the original African guitar [EXTERNAL]

More on…

We have LOTS of articles about music, audio and computer science. Have a look in these themed portals for more:

Getting technical…

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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