Data Sonification – using sound to make data easier to understand – is fascinating, and I think an incredibly powerful way to understand something quickly and instinctively – more so than just looking at the numbers alone.
The rising numbers of the coronavirus outbreak in the UK have become difficult to comprehend, so I wanted to use sound to create a more meaningful interaction with the statistics.
Listen out for the following:
Harp = Total cases | Violin = R number | Church Organ = new cases per month.
I used two tone for the data sonification after cleaning the data, then exported each ‘data song’ file into Logic Pro X to mix. The piece goes up at the end because of the rising case numbers. However, I felt the piece needed something extra to make it more ‘listenable’ and therefore easier to understand – instead of just hearing a series of musical notes. So the challenge: how do we balance any accompanying instruments – adding ambience and atmosphere without obstructing the data?
The hardest thing was choosing how to orchestrate the data itself: for fast moving numbers I felt the sound needed to be more percussive – but for the R number I felt there needed to be a more constant sound. I suspect there are some innate rules for data sonification I’m tapping into here, which might be interesting to research further.
Finally I used deep music visualiser to generate an #AI video which responds to the pitch and tempo, then onto Final Cut Pro X to edit on the captions and statistics to help listeners detect how the changes in pitch correlate to the numbers.
I hope that the next time I’m turning coronavirus data into music that the piece ends up lower at the end.
24-hour streaming AI-generated heavy metal on YouTube completely fascinated me – created by the eccentric Dadabots, half of whom I’ve regularly collaborated with on various strange musical projects. Their outputs inspired me to start my own journey of intersecting music with machine learning.
I’ve been composing since I was a kid on whatever platform I could find. Classically trained with a music degree while hungry for as much new music as possible makes for a strange hybrid, a musician and performer trying to understand a technologist’s world.
Amid much struggling and general frustration and many false starts, the stubbornness and late night wrangling paid off. I had my first track and plucked up the courage to share some of my experiments online.
So, here’s one of my first flirtations with Music and Machine Learning on Instagram – the Beatles singing ‘Call Me Maybe’ – because for some reason I thought it needed to exist. And, buoyed by my coding success, I learned how to generate some eye-bending video based on pitch and tempo too.
Each track takes quite a few hours to generate – even 45 seconds or so is a whole evening of attention. The way I’ve been doing it is heavily supervising the code, I need to intervene every few seconds to suggest a new direction for the algorithm in order for it to fit the direction I want it to go in. A lot of the decisions I’m making are not technical – they’re based on my musical knowledge. Then I listen repeatedly to the slowly lengthening audio to see if there’s a recognisable tune being created. Is it sounding like something a human can sing? Plus the ‘upsampling’ process, where some of the noise is removed, can take many hours. A lot of the time I’ll crash out of the virtual machine I’m using because I’m on the free tier. Sometimes I’ll lose everything.
Sounds frustrating, and it’s even more annoying in practice. Yet I find the ultimately infuriating nature of co-composing this way rather addictive. And, wow, when it actually does work, the results are incredibly rewarding.
So, ‘my’ new song made up of thousands of tiny bites of Beatles was compiled. And it is undeniably the Beatles singing ‘Call Me Maybe’ – so much so that a few of my friends thought this could easily be a demo tape or an unheard song if not for the lyrics.
My work received admiration from those familiar with AI music generation – they could tell how much effort was required to create it. And as well as praise, this short tune also generated unsettling feelings for others – which weirdly excited me – to have made something so conversation-worthy – especially in a field as wide as AI and Machine Learning felt like I was onto something, that my musical approach could add value in its own way.
Here’s another one – Queen singing ‘Let It Go’.
So why do I think this might make you question your musical tastes? Well, many of us are quite specific about the music we like. But if a fifty-year-old Beatles recording can be rehashed for a 21st Century Audience, would this track encourage a non-Beatles listener to explore more of this kind of music? Or would a devout 1960s music fan be persuaded to venture outside their comfort decade into the world of sugary pop music? I think it does.
Here’s U2 singing ‘Bat Out Of Hell’.
I’m surprised how much the original artist maintains their presence in each of these examples. And I’m somewhat tickled that the processing and supervision of each track makes this a very labour-intensive activity – not unlike standard music production.
As a new composing method, I am in awe of the sheer amount of work that must have gone into creating this program, and the brilliant minds behind it who conceived and created such a formidable tool for co-creation.
It even seems possible to train the AI on any kind of music as long as the artist has made enough material to be sampled adequately. Which is great news for those of us keen to create cross-cultural artworks – even though there are thousands of artists in the current Jukebox library, the content does appear to skew toward English-speaking music – a useful reminder that bias is built in to every system with humans at one end of it. So one of my next quests will be to see whether I can create my own training set (which might prove taxing on the free tier).
Finally, from a musical perspective, human composers still have quite a few advantages over machines, though generating music with AI is like a whole band writing all its parts at once, which can be very satisfying, if erratic. Sometimes the algorithm is temperamental – and doesn’t work at all. Other times, sublimely beautiful chords and ad-libs come out. No one can know whether the next track is a hit or a miss.
Even controlling the output is gloriously elusive: for example I can’t force a tune to go up or down at any point (though I can choose one of the alternatives that fits roughly where I’d like the tune to go). I don’t have much choice over the rate or meter of the lyrics – though there is some leeway when paginating them in the code. And changing the rate of intervention also affects what’s being generated – in short, the illusion of pulling order from chaos, a pleasing reflection of what composing music means to me.
In quite a few instances the AI has surprised me musically, and that is intriguing enough on its own for me to want to continue creating and co-composing with a machine. With so many possibilities in this field right now, I’m looking forward to exploring more.