This is Byzantine Chant: https://youtu.be/Bs--5yMg1g0
Also, Georgian Polyphony did not involve strings as a general rule. There were regional exceptions, but early Slavic polyphony was generally a capella.
Here's a good example of Gerogian polyphony: https://v-s.mobi/elia-lrdei-princeton-georgian-choirs-fall-2...
Sorry to nitpick, but I hate to see a fascinating corner of music ignored. Happy Listening!
But It's OK. I think the interesting thing is how the musical snippets are different from each other rather than whether or not they're correct in an absolute sense.
It's a step in the right direction, I think. And it's not surprising that the categorizations and recommendations you get on music streaming services like spotify aren't as ridiculously off the mark as they used to be.
https://twitter.com/GenreADay/status/1110510786277982208?s=2...
A more even-sounding spectrum is pink noise which is equal energy per base 2 logarithmic bandwidth. Sounds like a waterfall.
https://en.wikipedia.org/wiki/Ishkur%27s_Guide_to_Electronic...
One thing that's really funny is now with my current taste, going back on there and re-listening to the clips of artists that are now in my mainstay.
Lots of other cool Spotify-scraping projects by the author at the bottom.
"Subgenres are McDonald's business. By going over listener data and identifying patterns, McDonald and his co-workers can identify clusters of artists who might coalesce into a genre—something he’s been doing since his earliest days at the music-intelligence company The Echo Nest, which Spotify bought in 2014. Today, his work with Spotify's data helps listeners discover artists that may have been hiding in plain sight. McDonald’s “data alchemy” helps populate the Fans Also Like sections of Spotify's artist pages, as well as Daily Mix; it also provides a real-time chronicle of how music is developing and splintering into different styles."
[0] https://artists.spotify.com/blog/trap-queen-and-the-data-sci...
Edit: so out of curiosity I looked at the bottom right corner and found "tanci". Odd name. Clicked on it, and it's Chinese spoken word artists. Incidentally I practice Chinese so it's perfect.
IIRC, there are 14 dimensions in total, but it’s impossible to represent all of them on a page. So he went for up-down, left-right, clusters, and colors to represent a subset of them.
Source: used to work at Spotify.
If you still feel that you didn't get what you were looking for ... here's an acceptable substitute perhaps?
...but it IS still pretty cool.
+1 from me
Would be cool to see something like this on song-level, not artist/group.
This seems like a useful tool for discoveling new sounds, but when it comes to finding out what Polish free jazz really sounds like, I wouldn't trust it one bit.
https://www.vice.com/en_uk/article/68n44v/the-ill-fated-tale...
https://en.wikipedia.org/wiki/Divergent_series#Absolute_conv...
[edit] See my comment below: my joke is about the fact that, depending on the order in which you sum an infinite sequence of waveforms, you can create a sequence that converges to any sound you want [1] (as long as those waveforms together span the full frequency space). Note also that a sum over a truly continuous space of arbitrary waveforms is even more ill-defined.
Sound waves are indeed cancelled out by their inverse.
>The calibration is fuzzy, but in general down is more organic, up is more mechanical and electric; left is denser and more atmospheric, right is spikier and bouncier.
Not sure about the colors though.
This is perfect for me. So much more to discover.
Defining a genre by its characteristic instrument set, for instance, doesn't match how I tend to react to things very well, but it's a fairly popular way of separating genres, it seems. (I'm not saying I don't understand the use of that metric, it's very, well, available, in the sense of "availability heuristic". But I do not personally find it all that useful.)
It's a great music discovery service that I've used several times in the past. I've found some good artists this way, and really wish someone would build something similar for fiction books. The only downside is it's tied exclusively to Spotify.
You start at a band of your choice and then can travel all the bands in the world.
You can find it here, if anyone is interested: https://open.spotify.com/user/gallefray/playlist/6CzafKwRUi5...
Some of the genre memberships seem odd. Barry McGuire is bubblegum pop? As is Zager & Evans? Roy Orbison? That whole category seems to be a weird mashup of what I'd expect in bubblegum pop plus a random dump of '60s rock.
For artists that appear in more than one genre, it seems to use the same sample clip for all of them, so don't be put off from checking out an artist in a genre you like because the sample doesn't fit.
Good problem for adversarial learning. Train one ML system to rate EDM, trying to match some metric like total sales. Second system tries to generate EDM which gets high scores from the first system.
Here is an example in case you thought I was making up this genre:
for starters, no Kwela, Mbaqanga, Marabi, or Highlife. Allthough here is Kwaito
