The Picard team is happy to announce that Picard 2.7 Beta 1 is now available. This is a pre-release we put out for wider testing and to gather feedback on the changes before the final 2.7 release. There are many new features in this release, which might or might not work for you as expected. If you prefer stability we recommend you use the stable version Picard 2.6.4 which we released yesterday.
Please report any issue through our bug tracker and give us feedback on this beta release on the Community Forums.
Picard 2.6.4 is a maintenance release for Picard 2.6. It contains a couple of bug fixes, including possible crashes and startup issues on Windows. Users of Picard 2.6 are highly recommended to update.
This time we have a lot of small fixes for URLs, including URL seeding and cleanup, and a couple other small bugfixes. Nothing particularly big, but still hopefully useful!
A new release of MusicBrainz Docker is also available that matches this update of MusicBrainz Server. See the release notes for update instructions.
Thanks to atj, chaban, Cyberskull, danbloo, HDS, HibiscusKazeneko, mr_maxis and yyoung for having reported bugs and suggested improvements. Thanks to AO, Besnik, mfmeulenbelt, salo.rock, SpearDog and wa2c for updating the translations. And thanks to all others who tested the beta version!
We’re back to our usual two-week cycle with a relatively small set of changes, of which the most interesting might be that you can use some extra includes in the MusicBrainz API: release-group-level-rels for release lookups, and recording-level-rels and work-level-rels on release browses. Keep in mind that recording level relationships are still restricted to the same 500 recording limit that applies to very large release lookups, to keep the size of the API responses somewhat sensible – anything over 500 recordings and you’ll need to get the relationships entity by entity.
Additionally, we found an issue where the “make my tags private” setting was not being respected when users tried to navigate straight to a tag page, such as https://musicbrainz.org/user/username/tag/tagname, making it possible to see what the user in question had tagged with tagname even if the tags were meant to be private. This was fixed on both the beta and production servers as soon as we were made aware of it by CatQuest, but we cannot guarantee that it was never used by any too-curious editor who failed to report it before. We apologize for any possible breach of your tag privacy.
A new release of MusicBrainz Docker is also available that matches this update of MusicBrainz Server. See the release notes for update instructions.
Thanks to atj, CatQuest, chaban, Cyberskull, jesus2099, MrClon, mr_maxis and outsidecontext for having reported bugs and suggested improvements. Thanks to mfmeulenbelt and salo.rock for updating the translations. And thanks to all others who tested the beta version!
This release also contains quite a few bug fixes and improvements, given the amount of time since last release. Several fix guess-case issues, and we’ve refactored the guess-case code to be more maintainable in the future. A visible improvement you may notice is that we now display icons next to entity links in relationships to help distinguish different types of entities. (If you’ve been editing on MusicBrainz for a long time, you may remember something similar from the classic, pre-NGS website.)
A new release of MusicBrainz Docker is also available that matches this update of MusicBrainz Server. See the release notes for update instructions.
Thanks to aerozol, angriestchair, Beckfield, bsammon, chaban, CyberSkull, danBLOO, Dibou, Freso, HDS, jesus2099, KRSCuan, mr_maxis, mtrolley, navap, salo.rock, Shepard, yindesu, and yyoung for having reported bugs and suggested improvements. Thanks to Besnik, ffff23, kellnerd, mfmeulenbelt, panos, salo.rock, th1rtyf0ur, V6lur, and yoshi818 for updating the translations. And thanks to all others who tested the beta version!
We’re pleased to announce that we have just released acoustic similarity in AcousticBrainz. Acoustic similarity is a technique to automatically identify which recordings sound similar to other recordings, using only the recordings themselves, and not any additional metadata. This feature is available via the AcousticBrainz API and the AcousticBrainz website, from any recording page. General documentation on acoustic similarity is available at https://acousticbrainz.readthedocs.io/similarity.html.
This feature is based on work started by Philip Tovstogan at the Music Technology Group, the research group that provides the essentia feature extractor that powers AcousticBrainz. The work was continued by Aidan Lawford-Wickham during Summer of Code 2019. Thanks Philip and Aidan for your work!
From the recording view on AcousticBrainz, you can choose to see similar recordings and choose which similarity metric you want to use. Then, a list of recordings similar to the initial recording will be shown.
These metrics are based on different musical features that the AcousticBrainz feature extractor identifies in the audio file. Some of these features are related to timbral characteristics (generally, what something sounds like), Rhythmic (related to tempo or perceived pulses), or AcousticBrainz’s high-level features (hybrid features that use our machine learning system to identify features such as genre, mood, or instrumentation).
One thing that we can immediately see in these results is that the same recording appears many times. This is because AcousticBrainz stores multiple different submissions for the same MBID, and will sometimes get submissions for the same recording with different MBIDs if the data in MusicBrainz is like this. This is actually really interesting! It shows us that we are successfully identifying that two different submissions in AcousticBrainz as being the same using only acoustic information and no metadata. Using the API you can ask to remove these duplicated MBIDs from the results, and we have some future plans to use MusicBrainz metadata to filter more of these results when needed.
What’s next?
We haven’t yet performed a thorough evaluation of the quality of these similarity results. We’d like people to use them and give us feedback on what they think. In the future we may look at performing some user studies in order to see if some specific features tend to give results that people consider “more” similar than others. AcousticBrainz has a number of additional features in our database, and we’d like to experiment with these to see if they can be used as similarity metrics as well.
The fact that we can identify the same recording as being similar even when the MusicBrainz ID is different is interesting. It could be useful to use this similarity to identify when two recordings could be merged in MusicBrainz.
The data files used for this similarity are stand-alone, and can be used without additional data from AcousticBrainz or MusicBrainz. We’re looking at ways that we can make these data files downloadable so that developers can use them without having to query the AcousticBrainz API. If you think that you might be interested in this, let us know!
Hi! I am Jason Dao, aka jasondk on IRC. I’m a third year undergrad at University of California, Davis. This past summer, I’ve been working with the MetaBrainz team to add some neat features to the project ListenBrainz.
I am Akshat Tiwari (akshaaatt on IRC), an undergraduate student from Delhi Technological University, India.
It has been an exhilarating experience for me, right from submitting a proposal for GSoC to becoming a part of a fantastic community.
The Google Summer of Code 2021 Edition finally comes to an end after the 3-Month long journey. I will be detailing the journey of working towards my summer of code project today. This blog is a summary of all the work done.
Hello everyone, I’m Yang Yang (aka yyoung), an undergraduate student from Shanghai Jiao Tong University, China. I am honored to be accepted as a student of Google Summer of Code 2021 in MetaBrainz Foundation to work on the improvements of external links editor. I had a good time with the MusicBrainz dev team this summer, and it was a valuable experience for me. This is a final report and overview of my work.
Hi Everyone! I am Ritiek Malhotra (ritiek on IRC) and recently completed my undergraduate degree in Computer Science and Engineering. I participated in Google Summer of Code ’21 and worked on musicbrainz_rs – a library wrapper on the MusicBrainz Web API written for the Rust programming language.
