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.
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.
Our new study opens the question of the local music promotion within the digital environment. The Slovak Performing and Mechanical Rights Society (SOZA), the State51 music group in the United Kingdom, and the Slovak Arts Council commissioned Reprex to created a feasibility study which provides recommendations for better use of quotas for Slovak radio stations and which also maps the share and promotion of Slovak music within large streaming and media platforms such as Spotify.
Why are the total market shares of Slovak music relatively low both on the domestic and the foreign markets? How can we measure the market share of the Slovak music in the domestic and foreign markets? We offer some answers and solution based on …
The problem of the music industry is not too little, but too much data. Music is drowning in numbers, and it has too little resources to turn much data into valuable information. We have shown that we open collaboration is the key to success.
The Digital Muisc Observatory monitors the music markets with an economic methodology: we not only measure market volumes and prices, but we also measure both demand- and supply side indicators so that we can forecast future market volumes or prices.
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.