I’ve had time on my hands this past week and so I’ve wandered into making a few tracks of music. Here are two songs from yesterday, each capturing a little different emotional spin from being stuck temporarily at home. (Note: I think the songs are best experienced in headphones.)
Image collage created by AI via Bored Humans website
This post is really just an attempt of mine to gather together some of the explorations I have been doing to see what progress is being made with AI technology and the making of music and sound. It’s all pretty strange worlds out there right now.
Some sites are starting to use inputted text from users to generate sound and music. Others are built where the user does not have agency to create music, only to experience songs based on some choices, like mood or genre or artist. None of it, to my ears, sounds authentically human (yet?).
Here are a few sites I’ve explored:
Riffusion takes a phrase of writing put into its engine and moves it into a sort of song pattern, and the site features a rolling musical pattern that is designed to make visual the algorithmic patterns being used. Here is one that I did for a Daily Create that featured Riffusion with the phrase: A haiku is a moment in time. See the details on how Riffusion works — it is pretty fascinating. (Free to use)
Google is developing its own text-to-music AI generator, called MusicLM, which takes inputted text and creates a moody, evocative soundtrack to go with the words. There are plenty of intriguing examples, although it seems like Google is maybe working to figure out the copyright logistics of its database, where it has collected sound and loops that its machine uses to generate the soundtracks from text. Google also has the Magenta Project, another AI development that’s been around for bit, and the Magenta site does feature some examples of how it is being used to merge creativity with machine learning. (MusicLM not yet publicly available for use other than samples – Magenta data can be used for projects, I think)
OpenAI — the group behind ChatGPT — has Jukebox on its drawing board, and the algorithms are being fed music and sound and song lyrics, and it is learning how to create music tracks in the styles of those artists. It’s a bit jarring, to me, to hear how the machine uses Ella Fitzgerald as its source. OpenAI also has something called MuseNet, which seems similar to Jukebox. (Not yet publicly available other than samples)
The Bored Humans website has an AI Music Generator that uses neural network learning to produce entirely computer-generated songs, with lyrics. None of it is much fun to listen to for any extended period of time, in my opinion, but that it is being done is something to take note of, and worth playing around with. They even host a Lyric Generator. (Free to use)
Soundraw is a site that generates music by mood, and gives you a handful of song options to choose from. I found many of the tracks sounded the same, but I didn’t do a full explore of the possibilities there. Here is a track for the mood of “hopeful” as an example. (Free to use, but requires account for any download)
Melobytes is another odd one, but it gives you plenty of options to manipulate the sounds the AI generates as a sort of “song” from text — although every time I have used Melobytes, the song sounds pretty jarring and off-kilter to me. (Account required).
And I am sure there are others out there, too, and more to come, no doubt.
Whether this is all good for the field of music creation or not will be debated for a long time, I am sure. Just as ChatGPT and its field of chatbots has many thinking deeply on the art and creative act of writing, so too will the field of AI music generators have musicians wondering about how the field of sound is being transformed by algorithms, and what it means to be an artist in the field of music. (I also worry these AIs will put a lot of musicians who work on films and television and other media venues out of work, just as the DJ put many live bands out of work for weddings and other gigs.)
My connected friend, Maha Bali, shared a post about thinking through how to navigate the world of citation in the age of ChatGPT and AI-generated text, particularly if a writer uses the AI chat for parts of their own writing and/or research. Maha suggested a citation format, but I was particularly intrigued by Alan Levine’s thoughtful response in the comments, and when Alan referenced Jonathan Portiz’ insights about how or whether to reference machines in the act of writing, using the use of music software for songwriting as an example, something perked up for me.
I like to write and produce music, mostly at the hobby level. Although I do play real instruments (saxophone and rhythm guitar, and I also dabble with bass and keyboards), I also often turn to apps and platforms like Garageband and Soundtrap, and use sound loops and other elements of the computer to create music.
When I have shared those pieces out, I have often wrestled with how to make sure anyone listening (if anyone is even listening) would know it wasn’t me playing those instruments, but some musical loops. Often, of course, it’s obvious, mostly because the music comes out rather way too flawless and always exactly on the beat, like a droning metronome. That said, it’s not always obvious that technology has been used. If I am layering in my own singing voice, or my saxophone, or guitar into the mix, then the hybrid pieces are a bit of both things — the human musician and the algorithmic loops.
I have yet to come to a suitable system for letting anyone listening know that a piece of music is more computer loop than musician me. To be honest, I often travel the lazy route — no mentions of the software.
Here’s an example of what I mean. A music friend had sent me some lyrics and asked for a song, which I then built musically in Garageband after adding some lyrics to his words myself, so it’s a human-human-machine collaboration. When I shared the final version with him, he admired my guitar playing, to which I let him know the reality – none of it was me.
So this topic of leaning on the machine for creativity, and whether to make that kind of technical support more visible to others in any published content through citations or some other methods, has long been at the back of my mind.
This has been made more pertinent in recent years as my teenage son has been producing his own music tracks using another (more advanced) digital music software platform, collaborating with hiphop singers and writers from around the world. He doesn’t play an instrument. He plays the platform. He doesn’t cite the platform when he posts his music on the major streaming services.
Should he be considered a musician, even though he didn’t make any of the original loops himself? What about if he edits and changes the loops, as he does? Should every loop he uses be cited somehow?
All this brings us to ChatGPT and its brethren, and Maha’s exploration of how to make citations when using AI chat platforms for writing pieces.
Is it necessary to cite the machine?
My initial impulse is that Maha’s discussion about writing and citation feels different from making songs because it is writing of words through predictive text of the AI and not music composition with prerecorded loops. Writing a poem or a story or an essay also feels different than writing a song that layers words over music.
Even as I write that, though, I realize: that statement doesn’t seem to sit well with me at all — all are creative acts that begin with nothing but an idea and lead to something that others can experience. Maybe my conflicted feelings stem from being so used to technology being integrated so fully into the modern field of music production, and I am not yet used to its use in the field of writing.
Not yet, anyway. Will time and experience change that?
Garageband and Soundtrap and others don’t cite the musicians where the original loops came from. Do we expect that ChatGPT and others will cite where their words come scraped from? I believe that to be a strong yes in my view – that such information about original sources should be baked into the chart system (even as I understand the technical aspects will make such a thing nearly impossible). If this were done, then a writer could cite the sources of their AI-influenced writing.
How confusing is all this? Pretty.
And how interesting to grapple with it? Very much so.
I was working on a song the other day, started early after a restless night (not all that unusual for me), and needed a title. I came up with Slow Walking Through Sleep, and as I was pulling the music beneath an image, words to a poem began to filter in. So I added that, too. I debated about whether I should add my voice, reading the poem, but opted against it, to allow the music that started it all to shimmer through.
A few years ago, my friend John and I wrote and went into a studio, recording an original Christmas song, and each year, I bring it back out, and try to add some new remix elements as promotion. The song is called A Gift Of Peace (for Christmas) and I hope, if you listen, it brings you some joy.
This year, I followed some instructions from Eric Curts on making words light up as animation inside Google Slides. The original video – produced by my elder son, Colin – and full song is down below.
And if you want to help us earn nearly nothing but parts of pennies from streaming services, the song is on:
CLMOOC friends gathered and created artwork for a collective calendar for the 2022 year nearly a year ago now. Download it for free, if interested. I composed a short piece of music for each month as my contribution, and I am sharing out each month’s track at the start of each month.
In my last composition for the calendar project, I spliced in a sax solo from a holiday song that my friend, John, and I wrote and recorded in the studio.
Give me a book about music, and I am a happy reader.
This new book by Susan Rogers and Ogi Ogas goes beyond that. This Is What It Sounds Like is a tour de force, a well-written invitation to think about our choices in the music we listen to and that we love in the moment and over time, and Rogers (who is the primary voice here) is the perfect tour guide.
Rogers’ background is impressive, beginning as someone who helped build recording studios, to a stretch of time as a producer/engineer with Prince, to a producer of many other artists, to her time now as a cognitive neuroscientist and professor at the Berklee College of Music in Boston. Ogas is a published author of books about the brain and the way we think.
The book weaves in and out of Rogers’ stories in the music recording field, but finds it anchors in some key areas as the book explores why we love the music we do, why everyone’s tastes in music will be different, and how we can expand our ideas of not just what art is but how art provides an opportunity to enrich our lives.
The chapter titles give an overview of the topics of music listening:
Form and Function
Falling In Love
In each section, the reader is given insights on the listening to music that is intriguing, with “Record Pulls” — the sharing of songs with others that gives an insight to someone else on your own personality. The songs we share with others say something about ourselves, and Rogers believes in the idea of “Record Pulls” to shine a light on not just our listening but aspect of our personalities. (You can even join the online Record Pull that they have set up at their website: https://www.thisiswhatitsoundslike.com/record-pull)
All in all, this book was beautifully written (a few sections veer deeper into brain science, in relation to music, but it was definitely approachable to the general reader) and the insights had me thinking in new ways on songs and artists and music that have defined who I am for years.
I highly recommend This Is What It Sounds Like. Plus, you can listen in to the Virtual Jukebox of songs referenced in the book.
My youngest son is a talented music producer and he just dropped his latest album today — Good Time. It is available on all of the main streaming services and showcases collaborations with other artists. My son, Rowan, did all of the music production. (note: there are explicit songs, so be warned.)