peatmoss

I noped out of Anathem when I realized learning the new language was going to be non-optional and non-trivial. I was also at a place where I needed my reading to be recreational rather than mind expanding.

Makes me wonder what Anathem looks like in other translations and whether... Mathic was it(?), was as much an issue in translation. Maybe I'll take another go at it. It's been years, and people that I respect greatly have told me it's one of their favorites.

I have their sleep tracker, and one of the more annoying features is that you must open the app on your phone for it to sync sleep data to Apple Health. This is exactly the opposite of the convenience I had in mind when I bought the thing.

Linux is permanently hard and unfinished, and no amount of reality will dissuade anyone from this prior belief.

The article's headline isn't well backed up by the article. The article more claims that advantages that men have in other sports are erased in ultra-endurance sport, leveling the playing field.

The headline question is only really addressed by the combination of two things in the article (the second being brought up much later, and you kind of have to connect the dots yourself a bit): 1/ women have begun setting records in endurance sports 2/ women constitute a minority of participants in such events.

The first point, beginning to win some records, doesn't by itself address the question of the headline. But taken together with the relative rarity of women they say participate could point to there being some evidence for "better than" vs "equal to." Or it could be that for the records won, the winners are truly exceptional.

Whatever that rule about headlines ending with a question mark notwithstanding, I always cringe a little at headlines like these that seem to wish for a conclusion that's not really the direction of the article.

And it's good. It also seems to have the most flexible plugin system of any DAW that has decent level of usage.

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!