MusicBrainz VS Every Noice at Once

It is really cool and useful. Interesting that you were able to gather enough data from users to make it work. I guess it was much less useful in the beginning? I thought of making something similar with data from https://musicbrainz.org/. - Source: Hacker News / about 1 month ago

What do you use to tag your music? I imagine if you use a consistent piece of software (mp3tag, musicbrainz picard), they're going to do it the same way. Or for example, maybe musicbrainz.org allows people to enter metadata in their database in UTF-8, so it's possible you might get different answers from there even though you're using a standard piece of software. I imagine something like picard could use a search... Source: 5 months ago

The MusicBrainz database usually has hq versions of most covers. Source: 10 months ago

When it comes to MusicBrainzPicard, it pulls data directly from https://musicbrainz.org/. If you are finding music you have that isn't listed in the database, you could help out by contributing to the database so that everyone gets access to that music. Source: 10 months ago

While this doesn't help with replacing the discussion you get on reddit, might I suggest people spend some time contributing to their favourite artists on https://musicbrainz.org/ ( https://en.wikipedia.org/wiki/Musicbrainz )? Many services & apps use MusicBrainz data, so you could help your favourites by making sure the information for their entries on MusicBrainz is as complete and accurate as possible! Source: 11 months 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 / 11 days 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 / 28 days 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 / 4 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 / 5 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: 5 months ago