It would be reasonably trivial to set up a bot that mass-imports metadata from Spotify to MusicBrainz (note that MB rules do not allow this, community cleanup from a single user doing this with another source, years ago, is still ongoing).
The value that MusicBrainz adds is the community editor who spent a few hours going through YouTube videos and wayback machine social links to figure out that Fog (Wellington, NZ, punk/post-punk) and Fog (Auckland, NZ, Post-Punk) are different bands - even if they share a Spotify profile. The editor that hunted down and listened to 5 compilations that have mixed up a radio edit and an original mix of a track, to find out which is which, and separate them in MB and make notes. [these are made up examples]
That's not to imply that these two projects are 'competing', or that the ISRC figure comparison isn't useful and correct. But community database + scraped data is apples and oranges. And a mixed fruit bowl is wonderful.
This does not work, because the “right-skewed” apostrophe is not appropriate for every case of “straight” apostrophe placement.
However, making every “skewed” apostrophe “straight” is very easy for a computer to do (e.g. MusicBrainz Picard ships with a setting for this). This is why the MB guidelines say to prefer correct typographical apostrophes.
Note that they are preferred and not required, so you can still enter straight ones, but another editor may update them. I honestly don’t see the problem, but I am very pedantic about other things so I guess I can imagine how seeing the curlies could rub you the wrong way!
In brief, Music Neighbourhood is a pretty visualisation that connects artists with a high ‘similarity score’, allowing you to dynamically move from artist to artist, exploring their connections. Similarity score is a system that uses listener data to find similar artists, based on listener data, which has already seen use in our Fresh Releases and the LB Radio beta.
Music Neighbourhood also has a few other interesting things under the hood - for instance, it makes use of ‘Huesound’ to dynamically change colours based on the artist’s top album art.
Why is this of interest to hackernews? As with everything MetaBrainz creates, this is open-source, and we would love to see Music Neighbourhood, or elements of it, pulled into other projects or hacked into something new! The depth of MusicBrainz and ListenBrainz data allows for a lot of expansion on the concept - for instance, exploring based on genre, popularity, or listeners, and different ways of presenting clusters and datapoints. We expect to keep building on Music Neighbourhood ourselves, time allowing, as well.
The summary of the 2023 MetaBrainz summit is out, containing concise(ish) rundowns of the past year in MetaBrainz projects. If you’re interested in open source data projects like MusicBrainz, ListenBrainz and BookBrainz (yes, books too!) this is the best way to see what we’ve been up to.
We also discuss our path for the next year - your input is always welcome on this front, whether you’re looking for specific datasets to plug into something you are working on, or you are after new user-facing functionality or improvements.
ListenBrainz has a young team of developers (contract and volunteer) who have a particular focus on, and love for, our Android app.
That team has hit a milestone recently - three social music feeds have been released in the 2.3.0 ListenBrainz app release (as well as other updates).
If you’re interesting in music data, social ecosystems, and how transparent and open-source organisations (e.g. the not-evil team) can make that combination work, they would love your input and thoughts.
Your local non-profit open-source metadata provider, the MetaBrainz Foundation, and Byta.com (fast and secure audio sharing), have released the last in their trio of ‘Metadata matters’ articles.
These articles break down the complex world of music metadata (and getting paid!) into three parts:
- The Basics of Metadata
- Why Metadata is Important in Music Today
- Payments, Copyright & Legal Matters
We would love to get this out to musicians (and others in the music industry), and of course all you data nerds will find this interesting as well. The current music metadata situation is pretty dire, with a situation that doesn’t benefit artists. Or as the third article puts it: “Happy to receive boatloads of money from services such as Spotify and YouTube, [industry giants] comfortably rest on the mess they have created, because it serves them very well, even if it doesn’t serve most artists.” Letting everyone know why metadata matters is half the battle!
We at MetaBrainz recently released a new dataset, with the very catchy title of ‘MusicBrainz Canonical Metadata’. We posted a link to it on Hacker News and accompanied it with the bold claim that a user could now build their own custom music tagging application, without a lot of effort!
You might think we put our feet in our mouths, but the joke’s on you! Our feet taste GREAT! And also because mayhem has written a quick tutorial, that tells you exactly how to write your own tagger, in Python, using our new open dataset. The tutorial has three steps!? That seems like too few, but I haven’t tested it, so it’s probably fine. I hope someone’s tested it.
ANYWAY, new music dataset, you can use it to make cool things, it’s free for non-commercial users, there’s a tutorial on how to use it via the link at the top of this page. You can view all the MetaBrainz datasets at https://metabrainz.org/datasets
tl;dr: …provides an easy and compact way to identify your own music files, or to match/correct music metadata. Looking up tracks on MusicBrainz can be a challenge, since you need to understand the schema. Not so with this dataset – import the dataset into your favorite datastore and start looking up tracks.
The MusicBrainz project by MetaBrainz has released their latest dataset, MusicBrainz Canonical Metadata. This dataset solves a number of problems involving matching music to the correct entry in the massive MusicBrainz database. Previously it has been difficult to programmatically identify the main (canonical) release of an album or song. This dataset solves the problem, for anyone interested in building their own music database, tagger application, or other music-related application.
The MusicBrainz database aims to collect all the metadata for all music that has ever been published. For popular albums and songs, which have been released many times, it can be hard to answer the question “which one is the main (canonical) entry?” Using the new dataset, a user can enter any release or recording MBID (MusicBrainz identifier), and match it to the canonical entry.
The tables included in the dataset contain all the string metadata necessary to make effective use of the dataset. Artist names, release names and recording names are all present, indexed against the MBID’s. This lowers the barrier for entry to music-based development considerably — anyone can now import the dataset into their favourite datastore, and start looking up tracks.
The MetaBrainz Foundation offers a number of different datasets, often under the Creative Commons Zero (CC0) licence. These datasets can be used to build applications, databases, or train machine learning algorithms/AI. MetaBrainz Foundation datasets power countless projects, and stand behind the scenes of many of today’s largest tech companies, such as Microsoft, Google, and Amazon. The MetaBrainz Foundation datasets are all available on the MetaBrainz datasets page. The MetaBrainz Foundation uses the new MusicBrainz canonical metadata dataset themselves, primarily in the tagging application MusicBrainz Picard, and the social music site ListenBrainz.
The value that MusicBrainz adds is the community editor who spent a few hours going through YouTube videos and wayback machine social links to figure out that Fog (Wellington, NZ, punk/post-punk) and Fog (Auckland, NZ, Post-Punk) are different bands - even if they share a Spotify profile. The editor that hunted down and listened to 5 compilations that have mixed up a radio edit and an original mix of a track, to find out which is which, and separate them in MB and make notes. [these are made up examples]
That's not to imply that these two projects are 'competing', or that the ISRC figure comparison isn't useful and correct. But community database + scraped data is apples and oranges. And a mixed fruit bowl is wonderful.