Last year we started working on features to improve data produced from information about recordings that you submit to AcousticBrainz. First part of it was a way to create datasets that are used to train high-level models. The next were dataset creation challenges.
We already have a significant number of datasets created by AcousticBrainz community. The list of public datasets is available at https://beta.acousticbrainz.org/datasets/list. A couple of days ago our experimental challenge has concluded. It was related to classifying music with and without vocals. You can see final results at https://beta.acousticbrainz.org/challenges/14095b3b-4469-4e4d-984e-ef5f1a55962c.
Your feedback on high-level data
The latest addition to AcousticBrainz is a way to provide feedback about high-level output that you can see on summary pages for recordings. After a model is applied to all of the AcousticBrainz data we can understand how well it performs on a larger scale. This should help us make further improvements to models and underlying datasets. Keep in mind that you need to be logged in with your MusicBrainz account to see this.
Survey about new features
To help us understand how well new features work for you, we created a survey for you to participate in. If you have used AcousticBrainz, please fill out the survey here: https://goo.gl/forms/Oh3a9INBCCsW2I1i1. It shouldn’t take more than 5 minutes. We’ll keep it open for about a week.
Your feedback is very much appreciated. Especially considering that we don’t have a lot of ways to collect it from people. Some come to IRC and tell us about issues they are having, some comment on blog posts or create tickets in JIRA. But at this point we need a better overview of the current state of the project.
Thank you! 🎶