Almost 60% of the global recording industry sales are made via streaming platforms. Given the enormity of choice on these platforms consumers are more and more relying on the recommendations of autonomous recommendation systems. But these recommendation systems do not only enhance the user experience on the consumer side, but they also decide the fate of the investments that composers, lyricists, producers, and performers made into the music. We are going to contribute to a research on how such systems may lead to potentially tilted competition field between the content providers, and more specifically, between major labels and independents.
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.
The idea behind Listen Local is simple: we want machine learning algorithms of Spotify, YouTube, or other services to learn more about Slovak music. In order to make machines learn about Slovak music, we have to make machine-readable tables of Slovak music for AI learners
Reprex's project, the automated Demo Music Observatory will be represented by Daniel Antal, co-founder of Reprex among other building bridges projects. This project offers a different approach to the planned European Music Observatory based on the principles of open collaboration, which allows contributions from small organizations and even individuals, and which provides higher levels of quality in terms of auditability, timeliness, transparency and general ease of use.
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 concludes that urgent consideration should be given to a user-centric payment system, as well as greater transparency of the factors underpinning playlist creation and of negotiated agreements.
Daniel Antal, co-founder of Reprex, was selected into 2021 Fellowship program of JUMP, the European Music Market Accelerator. Jump provides a framework for music professionals to develop innovative business models, encouraging the music sector to work on a transnational level. The European Music Market Accelerator composed of MaMA Festival and Convention, UnConvention, MIL, Athens Music Week, Nouvelle Prague and Linecheck support him in the development of our two, interrelated projects over the next nine months.
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.
While the US have already taken steps to provide an integrated data space for music as of 1 January 2021, the EU is facing major obstacles not only in the field of music but also in other creative industry sectors. Weighing costs and benefits, there can be little doubt that new data improvement initiatives and sufficient investment in a better copyright data infrastructure should play a central role in EU copyright policy. Preprint of our article with copyright researchers.
Listen Local is a trustworthy, ethical AI-powered system that aims to help great artists in small organizations and small countries using big data. We want to make sure that audiences are not only recommended global superhits, but locally relevant music, too. At present, corporate algorithms fail to connect listeners in small countries with music from the local scene - with artists whom the listener can easily see perform live in local venues, who sing in the listener’s language and who connect with the listener’s feelings and experiences.
Listen Local is a trustworthy, ethical AI-powered system that aims to help great artists in small organizations and small countries using big data. We want to make sure that audiences are not only recommended global superhits, but locally relevant music, too. At present, corporate algorithms fail to connect listeners in small countries with music from the local scene - with artists whom the listener can easily see perform live in local venues, who sing in the listener’s language and who connect with the listener’s feelings and experiences.