Development/Summer of Code/2022/ListenBrainz

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< Development‎ | Summer of Code‎ | 2022
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ListenBrainz is one of the newest MetaBrainz projects. Read more information on its homepage.

Getting started

(see also: Getting started with GSoC)

If you want to work on ListenBrainz you should show that you are able to set up the server software and understand how some of the infrastructure works. Here are some things that we might ask you about

  • Show that you understand the goals that ListenBrainz wants to achieve, which are written on its homepage
  • Create an oauth application on the MusicBrainz website and add the configuration information to your ListenBrainz server. Use this to log in to your server with your MusicBrainz details
  • Use the import script that is part of the ListenBrainz server to load scrobbles from to your ListenBrainz server, or the main ListenBrainz server
  • Use your preferred programming language to write a submission tool that can send Listen data to ListenBrainz. You could make up some fake data for song names and artists. This data doesn't have to be real.
  • Try and delete the ListenBrainz database on your local server to remove the fake data that you added.
  • Look at the list of tickets that we have open for ListenBrainz and see if you understand what tasks the tickets involve
  • If you want to, see if you can contribute to fixing a ticket. Either add a comment to the ticket or ask in IRC for clarification if you don't understand what the ticket means

We're adding a number of new social features to ListenBrainz that we hope will enable people discover more music they like and users who have similar music tastes to their own. We're working on some of these features now, but we will need to get help for other features:

Create html pages that display data missing from MusicBrainz

Proposed mentors:mayhem, iliekcomputers
Languages/skills: Javascript, React, Python/Flask, Postgres

In the LB database we've been collecting metadata about artists, releases and recordings that may be missing from MusicBrainz. This project needs to add some new python flask views and react components to display what is known about the missing data we've collected. These pages should then explain to our users why they should be adding this data to MusicBrainz (it will make their stats/recommendations better) and then provide links to MB pages that allow pre-populating the missing data into the MusicBrainz data submission pages. For adding releases, the release editor should be seeded according to the release editor seeding guidelines.

Create a music recommendation algorithm using the Troi toolkit

Proposed mentors:mayhem
Languages/skills: Python, possibly Postgres.

Our troi recommendation toolkit is our playground for developing recommendation algorithms. The toolkit already knows how to fetch data from ListenBrainz for stats, collaborative filtered recommended tracks and from MusicBrainz for metadata and from AcousticBrainz for tracks that have similar acoustic features. We're looking for one student who has an original idea that can be implemented in Troi, ideally using the existing data-sets without having to invent or create new data-sets. This plugin should create a new feature that allows users to discover new music. Please note: We're going to be very selective on what proposals we accept for this project. Before you propose an algorithm to us, you'll need to carefully familiarize yourself with the troi toolkit and what features it provides. Your idea needs to be new and novel, at least in the context of Troi.

Integrate more music services for recording listens and playing music

Proposed mentors: _lucifer
Languages/skills: Python/Flask, Typescript/React

LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB also supports linking a Spotify account to record listening history. Currently, we are reworking the integration of external music service in LB to make adding other music services easier. We have looked into some other services and found that Deezer and Apple Music also provide the music playback and recording listening history capability. Integrating these services into LB would make for a good SoC project.