Thank you for this! Both MEI format and the Verovio engraver are news to me. I will check them out.
My first thought was whether MEI format is being added to MuseScore (the sheet music editor I use these days). It looks like it is: https://music-encoding.org/musescore-doc/
As a somewhat related aside, now that the MuseScore people own Hal Leonard and seem to pushing integration with their cloud subscription service, I wonder if they'll see some of these directions as potentially competing with them. I don't think there's anyone who wouldn't love a transposable clean digital version of their Real Books... and if Hal Leonard is in the business of selling Real Books, I can see where good OMR might be a problem for them. I guess piracy of scanned versions is already rampant, so maybe it's a wash.
It's just an entrenched aesthetic preference. Jazz fonts (fonts in this context refers both to the words and the music symbols) tend to be quite heavy with thick lines. I've heard that the thick hand-written style was originally to make charts more readable in dimly lit clubs, but with tablets and such, that's an anachronism now.
You can look at samples of Hal Leonard's Real Book(s) on their website to get a sense of what it looks like. Again, just an aesthetic preference, but one I and many others hold nonetheless.
I recently left a job at where I was working with open data producers / providers across a lot of domains. A lot of data is produced and released for free by governments and nonprofits because it's either directly part of the mission, or it's a natural byproduct of the organization's mission. Occasionally, you'd have really great datasets come out of industry / commercial organizations because the data were a byproduct and didn't create a scenario where a data release would create opportunity for competition.
I've been thinking about what kind of organization could be self-sustaining and also produce good music AI training data as a natural byproduct. An ideal arrangement would be something that provided some incentive or benefit to musicians in exchange for their recorded interpretation of sheet music. Soundslice, mentioned by another user, seems to do that. They let both teachers and students upload recordings of music that has been turned into MusicXML. The recordings, paired to those snippets of sheet music, has to be a gold mine. Assuming they have enough users. If they aren't already working on stem separation and automatic transcription, they probably should be. Still, my hope would be to figure out some kind of sustainable model where that dataset could be created and released for open model development...
As a domain, I see AI in music as a boon to human creativity. I am very much a novice jazz improvisor, and a passable amateur technician on the trombone. Human instructors can do a lot for me, but there's a lot that is "grinding it out" repetition, where I think AI could be a huge aid. I heard Sam Harris on a podcast recently talk about his bullishness on the humanities (paraphrasing: people don't care if a human reads their MRI if detection is good, but people probably do care that a human wrote the novel they're reading).
Music might even be a better example of the irreplaceability of people. While some people might bop along to a tune composed by Suno on the radio, live music is just so much more enjoyable for me. And even better than listening to a live show played by masters, is playing together with friends. To the extent that AI can patiently help us learn the skills to express our own creativity, I'm here for it!
I just signed up a trial, and uploaded a messy Real Book scan. It did very well! It missed the coda markings, but then again the directive in the Real Book was nonstandard. I guess that's a case where a multimodal model might have been able to read the text ("after solos, D.C. al coda") and do something smarter.
I'd imagine that rendered audio that just used midi voices (even high quality "Real Instruments" midi voices) would be pretty brittle for e.g. stem separation or automatic transcription. In a best case, I think you'd start with a clean digital representation, render sheet music imagery, and then have lots of recordings by a bunch of real instrumentalists playing the same music.
On the topic of stem separation, I've wondered about creating a quasi-synthetic dataset by taking chunks of recordings by real musicians playing them back in a real space in various combinations and recording the resulting analog-blended cacophony. Could repeat in various environments like cathedrals, basement bars, etc for realism :-)
I forgot to mention ABC. I have seen a few LLMs look at that. There was a model / paper published a couple years back called ChatMusician that built around it.
With the caveat that I'm not terribly fluent in ABC, it seems to me that simple things are simple, but hard things seem to be nearly pathological. And (again, maybe a lapse in my understanding) it seems like there may be a fair number of concepts that are impossible to convey in ABC?
Lastly, if I understand correctly, ABC got its start and is mostly popular as a simplified format for church songbooks. I'd imagine that would, uh, influence the training corpora towards sounding a bit... church songbooky.
EDIT: I may have been overly dismissive of ABC on first glance. It does seem like people have extended it quite a bit, and that it's at least, in theory, capable of encoding most of what I'd expect. And it's human readable, which is a benefit. Though, readability does take a stiff penalty the more richness you add (e.g. dynamics, articulations, stacked notes, etc)
I recently bought a tablet for sheet music, mostly to replace a stack of jazz "Real Books" at jam sessions. And the phone camera scans I made are okay, but fixed in size and have a lot of artifacts. And it would be great to transpose on the fly for e.g. Bb or Eb instruments, but being a scan this is obviously not possible.
I got digging into the state of optical music recognition and came away concluding that music is basically a greenfield for AI wherever you look. Optical music recognition is pretty terrible. AI understanding of music theory is terrible (actually looking at music that is; LLMs do okay at text descriptions of theory concepts where you can imagine some online texts making it in).
I think the issue is that we still don't have great digital formats that encode the dots on paper that musicians read. Music notation is pretty rich. Midi doesn't capture all of what's needed for symbolic understanding, because it was mostly made for capturing aspects relevant for playback or performance. MusicXML seems to be the closest for a digital format that encodes the information a musician would want, but there aren't great corpora of training data that would connect a MusicXML representation to sheet music images or to audio. I think that's because MusicXML falls short of encoding enough information to engrave music. Tools like MuseScore need to track a bunch of layout information that isn't encodable in MusicXML. Lilypond format is less verbose that MusicXML and contains a bit more information that is useful to the score creators, but most people don't create sheet music in lilypond. (As an aside, Lilypond bums me out with the state of jazz fonts. I hate looking at "legit" scores in jazz context)
I realize this is mildly off topic, but every time I see people making incremental gains on OCR, which to my mind is pretty good, I am reminded of how abysmal OMR is.
Everyone I know who is a casual jazz musician / student uses iReal Pro for practicing soloing. It does an okay job. The styles are mediocre and mechanical, and not as good as Band-in-a-Box or as rich as JJazzLab with its ability to plug in Yamaha styles.
I also own 4 editions of a new entrant called "Quartet" that uses carefully recorded, individually tracked, time annotated recordings of jazz standards by a real band. Solo sections can be repeated an arbitrary number of times and within fairly constrained limits tunes can be transposed, sped up, or slowed down. Of course the more you stretch or pitch bend the recordings, the more it sounds like garbage.
Something like iReal Pro, where you can key in your chord changes, configure repeats, specify instrumentation, etc. but generate high quality backing would be almost a holy grail for musicians' solo practice.
I also imagine this kind of thing would be a near perfect case to demonstrate "neurosymbolic AI". Backing tracks are constrained by actual constraints, not vibes. Suno does some impressive things, but was useless in my experience for trying to create a backing track.
"iReal, but with AI-generated backing band" is an idea I've even considered trying to build, but honestly I'd be just as happy to buy this app (or contribute to an open source version). Someone build this!
Most instruments tend to pitch sharp or flat depending on the partial. I don't recall any music teachers giving advice that specific positions should routinely get adjustments, but instead that notes in a particular partial should be adjusted. For example, F above middle C should be flattened when played in 1st position to compensate for 6th partial tending sharp. Or the G in 2nd position above that F needing to be pulled in a bit to compensate for 7th partial tending flat.
Manufacturers have different philosophies around this as well. I have a vintage mid-1960s King 3b whose partials line up differently and require different adjustment from my modern XO 1634... and both of those horns are extremely similar .508 bore tenor trombones.
Curious what you're looking for in that position as the aperture for what DS do at AWS is quite broad (my current role). DM me if you want to chat—contact info in profile.
If you buy their brushless line, you can add a few decent tools to your lineup while using the cheap stuff for everything else. Same battery platform generally. I have a lot of their cheap stuff, plus a few good ones that see more use.
When I watched Incubus I remember him sounding very much like he was trying to speak Italian. My only basis for comparison are some podcasts in Esperanto I've listened to, and completion of the duolingo course (I've forgotten everything).
I worry about the Slate truck being DOA with expiration of incentives for EVs. Someone please tell me I'm wrong, because if they do deliver as promised, I'll be excited to buy one.
For me, I'm hoping it fills the mid-90s Isuzu Pup sized hole in my heart.
From the papers I've read, the stem separation models all seem to train off what seems like a fairly small dataset that doesn't have great instrument representation.
I wonder if you could assemble a big corpus of individual solo instruments, then permute a cacophonous mix of them. IIRC the main training dataset is comprised of a limited number of real songs. But I think a model trained on real songs might struggle with more "out there" harmonies and mixes.
MuseScore is good enough that I haven't bothered to check back with commercial vendors. I'm pretty novice with it, however, so perhaps Sibelius power users will disagree.
Tunable transparency mode sounds great, and I wish Apple would do something like this as first-party support.
As a casual trombone player, who often plays in louder settings, the airpods pro are almost excellent hearing protection. Passive (even "audiophile" or "concert") earplugs make me feel like I'm under water. Airpods Pro attenuate a lot of sound but don't feel so unnatural.
Unfortunately, they tend to drop my own sound out of the mix when sounds around me get louder.
I'd love a mode that selectively let in more trombone frequencies, or better, that mixed noise cancellation and transparency to give me more of a studio monitor effect. Maybe the airpods could figure out which sounds were mine via the buzzing sounds that propagate through my head from my lips.
I think the answer here involves licensing and Apple control of the infrastructure, but my first thought was, "I historically trust Apple with my data a bit more than I trust Google, how is this not just trusting Google with my data?"
Apple previously pitched a vision of local-first AI for privacy, but seems to have badly miscalculated the kind of customer experience they could provide. My personal experience is that Siri has suffered greatly.
Case in point, I like to listen to music in the car, and Siri now confidently starts playing artists whose names sound nothing like what I requested. Also maddening "Play [x] on Apple Music" "You'll need to authorize me to use Youtube Music"
Still I live with / pay for so much that is broken based on a kind of Apple privacy vibes inertia. Siri being wired up to more of my personal information plus Apple maybe shipping that data to Google is going to make me reevaluate that.
I don't think those work between iOS and KDE Connect. I would love to be able to type iMessages on my Linux desktop's computer. If I'm wrong about this not working, someone please let me know, but I've never been able to make messages work.
I can't remember where I read it, but I read that Signal's popularity was high (highest?) in Germany. Assuming I'm not misremembering or that the situation hasn't changed, it seems that Germans care enough about the issue to stake out a position.
My first thought was whether MEI format is being added to MuseScore (the sheet music editor I use these days). It looks like it is: https://music-encoding.org/musescore-doc/
As a somewhat related aside, now that the MuseScore people own Hal Leonard and seem to pushing integration with their cloud subscription service, I wonder if they'll see some of these directions as potentially competing with them. I don't think there's anyone who wouldn't love a transposable clean digital version of their Real Books... and if Hal Leonard is in the business of selling Real Books, I can see where good OMR might be a problem for them. I guess piracy of scanned versions is already rampant, so maybe it's a wash.