algorithms

New spotifyr R Package Release

After a very thorough modernization of the package’s exception handling, documentation, and code dependencies that I did in the last week, the spotifyr package has passed again the peer-review standards and it is back on CRAN. The package is an excellent starting to point for R newbies to try their hands on musicology analysis with a few keystrokes. And of course, it is an essential part of the research infrastructure of musicology worldwide in far more advanced applications.

Trustworthy AI: Check Where the Machine Learning Algorithm is Learning From

We do care what our children learn, but we do not care yet about what our robots learn from. One key idea behind trustworthy AI is that you verify what data sources your machine learning algorithms can learn from. As we have emphasised in our forthcoming academic paper and in our experiments, one key problem that goes wrong when you see too few small country artists, or too few womxn in the charts is that the big tech recommendation systems and other autonomous systems are learning from historically biased or patchy data.

Recommendation Systems: What can Go Wrong with the Algorithm?

In complex systems there are hardly ever singular causes that explain undesired outcomes; in the case of algorithmic bias in music streaming, there is no single bullet that eliminates women from charts or makes Slovak or Estonian language content less valuable than that in English.

Harness the Algorithm

Save the Date: We will present the Listen Local project in the Algorithms in Film, Television and Sound Cultures: New Ways of Knowing and Storytelling conference.

Music Streaming: Is It a Level Playing Field?

Our paper argues that fair competition in music streaming is restricted by the nature of the remuneration arrangements between creators and the streaming platforms, the role of playlists, and the strong negotiating power of the major labels. It …

What If Your Stream Count Would Count For The Artists?

Many people believe that if you play your favorite song again, the artist will receive more money. Unfortunately, this is not the case. But the French Centre National de la Musique imagines a world where this would happen.

Reprex Joins The Dutch AI Coalition

Reprex is committed to develop its data platforms, or automated data observatories, and its Listen Local system in a trustworthy manner. Our startup participates in various scientific collaborations that are researching ideas on future regulation of copyright and fair competition with respect to AI algorithms, and joined the Dutch AI Coalition to position the company and the Netherlands at the forefront of knowledge and application of AI for prosperity and well-being, respecting Dutch and European values.

Reprex Website

At last, Reprex has its own company website, leaving the two flagship project sites, the Demo Music Observatory and the Listen Local separate. We are back to blogging after a particularly difficult lockdown period.

Demo Slovak Music Database

We needed a database of Slovak music to show how that national repertoire is seen by media and streaming platforms, how can we give it greater visibility in radio and streaming platforms, and what are the specific problems why certain artists and music is almost invisible.

Listen Local: Why We Need Alternative Recommendation Systems

Regulating black box, private algorithms and data monopolies is only a first step to damage control. Deploying white, transparent algorithms and building collaborative or open data pools can only guarantee fairness in the digital platforms, in recommendations, and generally in the use of AI.

Listen Local: Open Collaboration Experiment & Feasibility Study

Big data creates injustice. We want to help small venues, independent small businesses, great artists and dedicated fans to make algorithms work for them. We create locally relevant recommendations and measure their effect.