Development/Summer of Code/2017/AcousticBrainz

From MusicBrainz Wiki

Proposed mentor: ruaok or alastairp
Languages/skills: Python, Postgres, Flask
Forum for discussion

Getting started

(see also: GSoC - Getting started)

If you want to work on AcousticBrainz 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 you could do to get familiar with the AcousticBrainz project and code:

  • Install the server on your computer or use the Vagrant setup scripts to build a virtual machine
  • Download the AcousticBrainz submission tool and configure it to compute features for some of your audio files and submit them to the local server that you configured
  • Use your preferred programming language to access the API to download the data that you submitted to your server, or other data from the main AcousticBrainz server
  • Create an oauth application on the MusicBrainz website and add the configuration information to your AcousticBrainz server. Use this to log in to your server with your MusicBrainz details
  • Look at the system to build a Dataset (accessible from your profile page on the AcousticBrainz server) and try and build a simple dataset

Join in on development

We like it when potential students show initiative and make contributions to code without asking us what to do next. We have tagged tickets that we think are suitable for for new contributors with the "good-first-bug" label. Take a look at these tickets and see if any of them grab your interest. It's a good idea to talk to us before starting work on a ticket, to make sure that you understand what tasks are involved to finish the ticket, and to make sure that you're not duplicating any work which has already been done. To talk to us, join our IRC channel or post a message in the forums or on a ticket.

Ideas

Here are some ideas for projects that we would like to complete in AcousticBrainz in the near future. They are a good size for a Summer of Code project, but are in no way a complete list of possible ideas. If you have other ideas that you think might be interesting for the project join us in IRC and talk to us about your ideas.

BigQuery upload and statistics

Skills: Python, design

We have some prototype code to upload the contents of AcousticBrainz to google bigquery. We would like to finish this project so that the data is available in bigquery. This involves:

  1. Finishing the upload tool, fixing any existing issues
  2. Check that the data that we have stored in bigquery is in a format apt for searching. The value that BQ brings for us is to be able to quickly search through all of our data quickly. We need to make sure that it's possible. This could involve surveys/emails with other in the musicbrainz/Music research community
  3. Once the data is uploaded, create an interactive statistics page for the AcousticBrainz website, showing images similar to our original blog post

Statistics and data description

We have a lot of data in AcousticBrainz, but we don't know much about what this data looks like. This task involves looking at the data that we have and finding interesting ways to show this data to visitors to the AB website. Part of the proposal for this task would be to look at and understand the data and come up with a list of recommended visualisations/descriptions. For many of the types of statistics that we want to show, it is infeasible to compute the data at every page load, therefore part of this task is to also come up with an appropriate caching system.

Here are a few ideas for statistics that we have thought of so far:

Automatic updating statistics page, containing data about our submissions:

  • Formats, year, reported genre, other tags (mood)?
  • BPM analysis
  • Compare audio content md5_encoded with mbids
  • Use the musicbrainz mbid redirect tables to find more duplicates
  • Lists of artists + albums/recordings for each artist

Visualize AB data - either a sub-dataset/list or all data in AB

  • distribution plots for all low-level descriptors
  • expectedness of features for each particular track (paper: Corpus Analysis Tools for Computational Hook Discover by Jan Van Balen)

2D visual maps

  • Improving visualization of high-dimensional music similarity spaces (Flexter)
  • 2d maps with t-Stochastic Neighbor Embedding (TSNE, but there are other approaches in the paper) with shared nearest neighbor distance normalization (against hubs)

New machine learning infrastructure

Skills: Python, C++, Machine learning, scikitlearn, postgres

We build what we call high level models in acousticbrainz, which are multiclass SVM models trained using libsvm wrapped in a custom library called gaia. Gaia performs its task well, but it is written in C++ and not easy to extend with different machine learning algorithms and new techniques like deep learning.

We would like to replace our model training infrastructure with scikit learn, which is widely known and contains a large number of machine learning algorithms

  1. Understand the existing gaia-based training process
  2. Reproduce the existing SVM model process using scikitlearn
  3. Replace the highlevel model training process with scikitlearn
  4. Perform an analysis of other ML algorithms in scikitlearn to see if they give better results than those that we currently have with SVM

Docker-based model training for 3rd party ML algorithms

The process of training models in AcousticBrainz runs on our servers. This is because it's a simple process, but also so that we can create estimates of new data when it submitted to us. Some researchers have shown interest in contributing machine learning algorithms to acousticbrainz (in contrast to datasets). We would like to be able to run their code on our servers so that we can calculate features for new submissions, but we want to be careful about running 3rd party code on our servers.

A solution to this would be to run 3rd party algorithms inside docker. Researchers can provide us with an image which has a known API, which we can call automatically.

Storage for detailed analysis files