I extracted the vocal stem using melody.ml so they already aren't great quality, right now I just have some basic equing and Soundgoodizer preset A on the master. Source: 12 months ago

Download his songs off youtube and then import em into melody.ml. Source: about 1 year ago

Since it seems pretty similar to 302 you could also isolate the frequencies or extract all the parts to see how he mixed it. melody.ml is a decent way to have a quick look at volume levels and sounds used IMO. Source: about 1 year ago

Have a wav or mp3, you could download from youtube using https://yt1s.com Remove vocals using Https://melody.ml Or for a download version: Https://makenweb.com/SpleeterGUI. Source: about 1 year ago

I'd study every single record of Midnight Star, D-Train, Shalamar, all that kind of stuff. Use https://melody.ml/ to isolate the bass so you can really hear what's going on. Source: over 1 year ago

I see this in https://everynoise.com/#updates > 2024-01-05 status update: With my layoff from Spotify on 2023-12-04, I lost the internal data-access required for ongoing updates to many parts of this site. Most of this, as a result, is now a static snapshot of what, for now, will be the final state from the site's 10-year history and evolution, hosted on my own server. Some pieces may get disabled and reenabled... - Source: Hacker News / 2 months ago

Anyone aware of a similar feature for foobar2000? I have an extensive library mostly tagged from Discogs, including release IDs. In theory, this should be sufficient to cluster music by genres, pull similar releases from Discogs "similar" feature and correlate data from https://everynoise.com. Obviously, in case of album mixed genres things will mix up, but I'm not sure there's a model that can correlate existing... - Source: Hacker News / 3 months ago

The article mentions Glenn McDonald's musical genre page (https://everynoise.com/, no longer refreshing with new Spotify data) as an example of a flexible graph-like exploration format, without being burdened by explicit connections. The author also has a thorough description of pros and cons of the general concept. - Source: Hacker News / 6 months ago

This is from Glenn McDonald's blog, founder of "Every Noise at Once". He was laid off from Spotify (discussed here briefly [0]) --- https://everynoise.com/ is now in "archival copy" mode [1][2]. Super sad to read / see this. [0] https://news.ycombinator.com/item?id=38650917 [2] https://twitter.com/EveryNoise/status/1736086849339244935. - Source: Hacker News / 7 months ago

Data exported using: https://benjaminbenben.com/lastfm-to-csv/ Album art compiled using: https://www.neverendingchartrendering.org/ Genre data compiled using: http://organizeyourmusic.playlistmachinery.com/# https://everynoise.com/ https://www.tunemymusic.com/transfer Gender, year and country of origin information manually compiled using Last.fm and wikipedia. Data analysis done in excel and image created in GIMP. Source: 7 months ago