Daniel Antal | Antal Dániel honlapja

Green Paper on AI, Data Governance, and Metadata Policies for Europe’s Music Ecosystem (v0.1 Early Release)

Mon, 08 Sep 2025 00:00:00 +0000

About this Release

This Green Paper is an open consultation draft produced by the Open Music Europe consortium as part of Horizon Europe Deliverable D5.7.
It has been released early in line with the principles of Open Policy Analysis (OPA) to make the drafting process auditable, invite feedback from stakeholders, and ensure transparency.

Important: This version is not for citation in academic or policy work. A stable version with a DOI will be released later in 2025 and will serve as the basis for Deliverable D5.7 (Policy Brief) and a subsequent White Paper.

Participate

We invite stakeholders from music, cultural heritage, and AI governance communities to comment and contribute to this draft.
Please visit the Zenodo record or the Open Music Europe website for more information.

Remapping the Livonian Coast: A Multilingual Gazetteer of the Settlements of Northern Kurzeme

Sun, 15 Jun 2025 11:00:00 +0000

Read our blogpost introducting the data paper: Metadata Groundhog Day: What a Moribound Language Can Teach Spotify and Shopify.

Open Music Registers

Tue, 29 Apr 2025 00:00:00 +0000

About this Release

This technical paper is part of the Open Music Observatory under the Horizon Europe Open Music Europe project.
It presents an early framework for federated music registers and demonstrates how they can support rights management, cultural statistics, and business innovation.

The current edition describes the design principles and pilot implementations.
Future editions will extend the model with more data partners, stress-tested pipelines, and additional use cases.

Note: This is a technical release and should be cited using the DOI: 10.5281/zenodo.14767717.

Participate

We invite music industry partners, cultural institutions, and researchers to engage with the pilot registers and help refine the model.
Please visit the Zenodo record or the Open Music Observatory for more information.

A szlovák adatkicserélési tér magyarországi föderációjának lehetőségei

Thu, 19 Dec 2024 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Supplementary notes can be added here, including code and math.

Open Music Observatory Technical Report (Versioned)

Tue, 30 May 2023 00:00:00 +0000

About this Release

This report presents the first technical foundations of the Open Music Observatory.
It was written before consortium partners supplied their datasets and before real data pipelines were stress-tested.

The document outlines the Observatory’s architecture, data governance approach, and integration strategy, but it remains an early edition.

Note: This version is preliminary and should not be cited as a final technical reference. A new, data-driven edition will be released in 2025 once the Observatory has been validated with live data from partners.

Next Steps

The upcoming edition will integrate real-world metadata, copyright, and economic indicators, stress-tested through operational pipelines, and will provide a more complete technical baseline for Europe’s music data space.

An Empirical Analysis of Music Streaming Revenues and Their Distribution

Thu, 23 Sep 2021 10:00:00 +0000

This report was commissioned by the Music Creators’ Earnings Project to provide an empirical analysis of music streaming revenues in the UK.

It showed that:

total market growth often hides flat or declining individual earnings,
exchange rate effects played a major role in sustaining incomes during 2015–2019,
and current remuneration schemes and pro-rata distribution systems do not adequately reflect the value of music for most rightsholders.

The study argued for international data harmonisation, better survey methods, and policy coordination to make earnings more transparent and equitable.

📄 Full report PDF
📄 Zenodo record

Economic and Environment Impact Analysis, Automated for Data-as-Service

Thu, 03 Jun 2021 16:00:00 +0000

We have released a new version of iotables as part of the rOpenGov project. The package, as the name suggests, works with European symmetric input-output tables (SIOTs). SIOTs are among the most complex governmental statistical products. They show how each country’s 64 agricultural, industrial, service, and sometimes household sectors relate to each other. They are estimated from various components of the GDP, tax collection, at least every five years.

SIOTs offer great value to policy-makers and analysts to make more than educated guesses on how a million euros, pounds or Czech korunas spent on a certain sector will impact other sectors of the economy, employment or GDP. What happens when a bank starts to give new loans and advertise them? How is an increase in economic activity going to affect the amount of wages paid and and where will consumers most likely spend their wages? As the national economies begin to reopen after COVID-19 pandemic lockdowns, is to utilize SIOTs to calculate direct and indirect employment effects or value added effects of government grant programs to sectors such as cultural and creative industries or actors such as venues for performing arts, movie theaters, bars and restaurants.

Making such calculations requires a bit of matrix algebra, and understanding of input-output economics, direct, indirect effects, and multipliers. Economists, grant designers, policy makers have those skills, but until now, such calculations were either made in cumbersome Excel sheets, or proprietary software, as the key to these calculations is to keep vectors and matrices, which have at least one dimension of 64, perfectly aligned. We made this process reproducible with iotables and eurostat on rOpenGov

Our iotables package creates direct, indirect effects and multipliers programatically. Our observatory will make those indicators available for all European countries.

Accessing and tidying the data programmatically

The iotables package is in a way an extension to the eurostat R package, which provides a programmatic access to the Eurostat data warehouse. The reason for releasing a new package is that working with SIOTs requires plenty of meticulous data wrangling based on various metadata sources, apart from actually accessing the data itself. When working with matrix equations, the bar is higher than with tidy data. Not only your rows and columns must match, but their ordering must strictly conform the quadrants of the a matrix system, including the connecting trade or tax matrices.

When you download a country’s SIOT table, you receive a long form data frame, a very-very long one, which contains the matrix values and their labels like this:

## Table naio_10_cp1700 cached at C:\Users\...\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds
# we save it for further reference here
saveRDS(naio_10_cp1700, "not_included/naio_10_cp1700_date_code_FF.rds")
# should you need to retrieve the large tempfiles, they are in
dir (file.path(tempdir(), "eurostat"))
dplyr::slice_head(naio_10_cp1700, n: 5)
## # A tibble: 5 x 7
## unit stk_flow induse prod_na geo time values
## <chr> <chr> <chr> <chr> <chr> <date> <dbl>
## 1 MIO_EUR DOM CPA_A01 B1G EA19 2019-01-01 141873.
## 2 MIO_EUR DOM CPA_A01 B1G EU27_2020 2019-01-01 174976.
## 3 MIO_EUR DOM CPA_A01 B1G EU28 2019-01-01 187814.
## 4 MIO_EUR DOM CPA_A01 B2A3G EA19 2019-01-01 0
## 5 MIO_EUR DOM CPA_A01 B2A3G EU27_2020 2019-01-01 0

The metadata reads like this: the units are in millions of euros, we are analyzing domestic flows, and the national account items B1-B2 for the industry A01. The information of a 64x64 matrix (the SIOT) and its connecting matrices, such as taxes, or employment, or C**O₂ emissions, must be placed exactly in one correct ordering of columns and rows. Every single data wrangling error will usually lead in an error (the matrix equation has no solution), or, what is worse, in a very difficult to trace algebraic error. Our package not only labels this data meaningfully, but creates very tidy data frames that contain each necessary matrix of vector with a key column.

iotables package contains the vocabularies (abbreviations and human readable labels) of three statistical vocabularies: the so called COICOP product codes, the NACE industry codes, and the vocabulary of the ESA2010 definition of national accounts (which is the government equivalent of corporate accounting).

Our package currently solves all equations for direct, indirect effects, multipliers and inter-industry linkages. Backward linkages show what happens with the suppliers of an industry, such as catering or advertising in the case of music festivals, if the festivals reopen. The forward linkages show how much extra demand this creates for connecting services that treat festivals as a ‘supplier’, such as cultural tourism.

Let’s seen an example

## Downloading employment data from the Eurostat database.
## Table lfsq_egan22d cached at C:\Users\...\Temp\RtmpGQF4gr/eurostat/lfsq_egan22d_date_code_FF.rds

and match it with the latest structural information on from the Symmetric input-output table at basic prices (product by product) Eurostat product. A quick look at the Eurostat website already shows that there is a lot of work ahead to make the data look like an actual Symmetric input-output table. Download it with iotable_get() which does basic labelling and preprocessing on the raw Eurostat files. Because of the size of the unfiltered dataset on Eurostat, the following code may take several minutes to run.

sk_io <- iotable_get ( labelled_io_data: NULL,
source: "naio_10_cp1700", geo: "SK",
year: 2015, unit: "MIO_EUR",
stk_flow: "TOTAL",
labelling: "iotables" )
## Reading cache file C:\Users\..\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds
## Table naio_10_cp1700 read from cache file: C:\Users\..\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds
## Saving 808 input-output tables into the temporary directory
## C:\Users\...\Temp\RtmpGQF4gr
## Saved the raw data of this table type in temporary directory C:\Users\...\Temp\RtmpGQF4gr/naio_10_cp1700.rds.

The input_coefficient_matrix_create() creates the input coefficient matrix, which is used for most of the analytical functions.

a_i**j: X_i**j / x_j

It checks the correct ordering of columns, and furthermore it fills up 0 values with 0.000001 to avoid division with zero.

input_coeff_matrix_sk <- input_coefficient_matrix_create(
data_table: sk_io
)
## Columns and rows of real_estate_imputed_a, extraterriorial_organizations are all zeros and will be removed.

Then you can create the Leontieff-inverse, which contains all the structural information about the relationships of 64x64 sectors of the chosen country, in this case, Slovakia, ready for the main equations of input-output economics.

I_sk <- leontieff_inverse_create(input_coeff_matrix_sk)

And take out the primary inputs:

primary_inputs_sk <- coefficient_matrix_create(
data_table: sk_io,
total: 'output',
return: 'primary_inputs')
## Columns and rows of real_estate_imputed_a, extraterriorial_organizations are all zeros and will be removed.

Now let’s see if there the government tries to stimulate the economy in three sectors, agricultulre, car manufacturing, and R&D with a billion euros. Direct effects measure the initial, direct impact of the change in demand and supply for a product. When production goes up, it will create demand in all supply industries (backward linkages) and create opportunities in the industries that use the product themselves (forward linkages.)

direct_effects_create( primary_inputs_sk, I_sk ) %>%
select ( all_of(c("iotables_row", "agriculture",
"motor_vechicles", "research_development"))) %>%
filter (.data$iotables_row %in% c("gva_effect", "wages_salaries_effect",
"imports_effect", "output_effect"))
## iotables_row agriculture motor_vechicles research_development
## 1 imports_effect 1.3684350 2.3028203 0.9764921
## 2 wages_salaries_effect 0.2713804 0.3183523 0.3828014
## 3 gva_effect 0.9669621 0.9790771 0.9669467
## 4 output_effect 2.2876287 3.9840251 2.2579634

Car manufacturing requires much imported components, so each extra demand will create a large importing activity. The R&D will create a the most local wages (and supports most jobs) because research is job-intensive. As we can see, the effect on imports, wages, gross value added (which will end up in the GDP) and output changes are very different in these three sectors.

This is not the total effect, because some of the increased production will translate into income, which in turn will be used to create further demand in all parts of the domestic economy. The total effect is characterized by multipliers.

Then solve for the multipliers:

multipliers_sk <- input_multipliers_create(
primary_inputs_sk %>%
filter (.data$iotables_row == "gva"), I_sk )

And select a few industries:

set.seed(12)
multipliers_sk %>%
tidyr::pivot_longer ( -all_of("iotables_row"),
names_to: "industry",
values_to: "GVA_multiplier") %>%
select (-all_of("iotables_row")) %>%
arrange( -.data$GVA_multiplier) %>%
dplyr::sample_n(8)
## # A tibble: 8 x 2
## industry GVA_multiplier
## <chr> <dbl>
## 1 motor_vechicles 7.81
## 2 wood_products 2.27
## 3 mineral_products 2.83
## 4 human_health 1.53
## 5 post_courier 2.23
## 6 sewage 1.82
## 7 basic_metals 4.16
## 8 real_estate_services_b 1.48

Vignettes

The Germany 1990 provides an introduction of input-output economics and re-creates the examples of the Eurostat Manual of Supply, Use and Input-Output Tables, by Jörg Beutel (Eurostat Manual).

The United Kingdom Input-Output Analytical Tables Daniel Antal, based on the work edited by Richard Wild is a use case on how to correctly import data from outside Eurostat (i.e. not with eurostat::get_eurostat()) and join it properly to a SIOT. We also used this example to create unit tests of our functions from a published, official government statistical release.

Finally, Working With Eurostat Data is a detailed use case of working with all the current functionalities of the package by comparing two economies, Czechia and Slovakia and guides you through a lot more examples than this short blogpost.

Our package was originally developed to calculate GVA and employment effects for the Slovak music industry (see our Slovak Music Industry Report), and similar calculations for the Hungarian film tax shelter. We can now programatically create reproducible multipliers for all European economies in the Digital Music Observatory, and create further indicators for economic policy making in the Economy Data Observatory.

Environmental Impact Analysis

Our package allows the calculation of various economic policy scenarios, such as changing the VAT on meat or effects of re-opening music festivals on aggregate demand, GDP, tax revenues, or employment. But what about the C**O₂, methane and other greenhouse gas effects of the reopening festivals, or the increasing meat prices?

Technically our package can already calculate such effects, but to do so, you have to carefully match further statistical vocabulary items used by the European Environmental Agency about air pollutants and greenhouse gases.

The last released version of iotables is Importing and Manipulating Symmetric Input-Output Tables (Version 0.4.4). Zenodo. https://doi.org/10.5281/zenodo.4897472, but we are alread working on a new major release. In that release, we are planning to build in the necessary vocabulary into the metadata functions to increase the functionality of the package, and create new indicators for our Green Deal Data Observatory. This experimental data observatory is creating new, high quality statistical indicators from open governmental and open science data sources that has not seen the daylight yet.

rOpenGov and the EU Datathon Challenges

rOpenGov, Reprex, and other open collaboration partners teamed up to build on our expertise of open source statistical software development further: we want to create a technologically and financially feasible data-as-service to put our reproducible research products into wider user for the business analyst, scientific researcher and evidence-based policy design communities.

rOpenGov is a community of open governmental data and statistics developers with many packages that make programmatic access and work with open data possible in the R language. Reprex is a Dutch-startup that teamed up with rOpenGov and other open collaboration partners to create a technologically and financially feasible service to exploit reproducible research products for the wider business, scientific and evidence-based policy design community. Open data is a legal concept - it means that you have the rigth to reuse the data, but often the reuse requires significant programming and statistical know-how. We entered into the annual EU Datathon competition in all three challenges with our applications to not only provide open-source software, but daily updated, validated, documented, high-quality statistical indicators as open data in an open database. Our iotables package is one of our many open-source building blocks to make open data more accessible to all.

Join our open collaboration Digital Music Observatory team as a data curator, developer or business developer. More interested in environmental impact analysis? Try our Green Deal Data Observatory team! Or economic policies, particularly computation antitrust, innovation and small enterprises? Check out our Economy Music Observatory team!

Recommendation Systems: What can Go Wrong with the Algorithm?

Thu, 06 May 2021 07:10:00 +0000

Traitors in a war used to be executed by firing squad, and it was a psychologically burdensome task for soldiers to have to shoot former comrades. When a 10-marksman squad fired 8 blank and 2 live ammunition, the traitor would be 100% dead, and the soldiers firing would walk away with a semblance of consolation in the fact they had an 80% chance of not having been the one that killed a former comrade. This is a textbook example of assigning responsibility and blame in systems. AI-driven systems such as the YouTube or Spotify recommendation systems, the shelf organization of Amazon books, or the workings of a stock photo agency come together through complex processes, and when they produce undesirable results, or, on the contrary, they improve life, it is difficult to assign blame or credit.

This is the edited text of my presentation on Copyright Data Improvement in the EU – Towards Better Visibility of European Content and Broader Licensing Opportunities in the Light of New Technologies - download the entire webinar’s agenda.

Assigning and avoding blame.

If you do not see enough women on streaming charts, or if you think that the percentage of European films on your favorite streaming provider—or Slovak music on your music streaming service—is too low, you have to be able to distribute the blame in more precise terms than just saying “it’s the system” that is stacked up against women, small countries, or other groups. We need to be able to point the blame more precisely in order to effect change through economic incentives or legal constraints.

This is precisely the type of work we are doing with the continued support of the Slovak national rightsholder organizations, as well as in our research in the United Kingdom. We try to understand why classical musicians are paid less, or why 15% of Slovak, Estonian, Dutch, and Hungarian artists never appear on anybody’s personalized recommendations. We need to understand how various AI-driven systems operate, and one approach would at the very least model and assign blame for undesirable outcomes in probabilistic terms. The problem is usually not that an algorithm is nasty and malicious; Algorithms are often trained through “machine learning” techniques, and often, machines “learn” from biased, faulty, or low-quality information.

Outcomes: What Can Go Wrong With a Recommendation System?

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. Some apparent causes may in fact be “blank cartridges,” and the real fire might come from unexpected directions. Systematic, robust approaches are needed in order to understand what it is that may be working against female or non-cisgender artists, long-tail works, or small-country repertoires.

Some examples of “undesirable outcomes” in recommendation engines might include:

Recommending too small a proportion of female or small country artists; or recommending artists that promote hate and violence.
Placing Slovak books on lower shelves.
Making the works of major labels easier to find than those of independent labels.
Placing a lower number of European works on your favorite video or music streaming platform’s start window than local television or radio regulations would require.
Filling up your social media newsfeed with fake news about covid-19 spread by some malevolent agents.

These undesirable outcomes are sometimes illegal as they may go against non-discrimination or competition law. (See our ideas on what can go wrong – Music Streaming: Is It a Level Playing Field?) They may undermine national or EU-level cultural policy goals, media regulation, child protection rules, and fundamental rights protection against discrimination without basis. They may make Slovak artists earn significantly less than American artists.

Metadata problems: no single bullet theory

In our work in Slovakia, we reverse engineered some of these undesirable outcomes. Popular video and music streaming recommendation systems have at least three major components based on machine learning:

The users’ history – Is it that users’ history is sexist, or perhaps the training metadata database is skewed against women?
The works’ characteristics – are Dvorak’s works as well documented for the algorithm as Taylor Swift’s or Drake’s?
Independent information from the internet – Does the internet write less about women artists?

In the making of a recommendation or an autonomous playlist, these sources of information can be seen as “metadata” concerning a copyright-protected work (as well as its right-protected recorded fixation.) More often than not, we are not facing a malicious algorithm when we see undesirable system outcomes. The usual problem is that the algorithm is learning from data that is historically biased against women or biased for British and American artists, or that it is only able to find data in English language film and music reviews. Metadata plays an incredibly important role in supporting or undermining general music education, media policy, copyright policy, or competition rules. If a video or music steaming platform’s algorithm is unaware of the music that music educators find suitable for Slovak or Estonian teenagers, then it will not recommend that music to your child.

Furthermore, metadata is very costly. In the case of cultural heritage, European states and the EU itself have been traditionally investing in metadata with each technological innovation. For Dvorak’s or Beethoven’s works, various library descriptions were made in the analogue world, then work and recording identifiers were assigned to CDs and mp3s, and eventually we must describe them again in a way intelligible for contemporary autonomous systems. In the case of classical music and literature, early cinema, or reproductions of artworks, we have public funding schemes for this work. But this seems not to be enough. In the current economy of streaming, the increasingly low income generated by most European works is insufficient to even cover the cost of proper documentation, which then sends that part of the European repertoire into a self-fulfilling oblivion: the algorithm cannot “learn” its properties and it never shows these works to users and audiences.

Until now, in most cases, it was assumed that it is the artists or their representative’s duty to provide high quality metadata, but in the analogue era, or in the era of individual digital copies, we did not anticipate that the sales value will not even cover the documentation cost. We must find technical solutions with interoperability and new economic incentives to create proper metadata for Europe’s cultural products. With that, we can cover one area out of the three possible problem terrains.

But this is not enough. We need to address the question of how new, better Algorithms can learn from user history and avoid amplifying pre-existing bias against women or hateful speech. We need to make sure that when Algorithms are “scraping” the internet, they do so in an accountable way that does not make small language repertoires vulnerable.

Incentives and investments into metadata

In our paper we argue for new regulatory considerations to create a better, and more accountable playing field for deploying Algorithms in a quasi-autonomous system, and we suggest further research to align economic incentives with the creation of higher quality and less biased metadata. The need for further research on how these large systems affect various fundamental rights, consumer or competition rights, or cultural and media policy goals cannot be overstated. The first step is to open and understand these autonomous systems. It is not enough to say that the firing squads of Big Tech are shooting women out from charts, ethnic minority artists from screens, and small language authors from the virtual bookshelves. We must put a lot more effort on researching the sources of the problems that make machine learning Algorithms behave in a way that is not compatible with our European values or regulations.

This blogpost was first published on our general interest blog Data & Lyrics

Our Music Observatory in the Jump European Music Market Accelerator: Meet the 2021 Fellows and their Tutors

Thu, 04 Mar 2021 15:00:00 +0200

According to the announcement of JUMP, the European Music Market Accelerator, after a careful screening of all applications received, the selection committee composed of all JUMP board members has selected the most promising ideas and projects to be developed together with renowned tutors for this 2021 fellowship.

For nine months, the 20 fellows living in many European countries will develop their innovative projects, while receiving a comprehensive 360° training. In addition to specialised workshops by highly qualified experts, each fellow will receive one-on-one tutoring sessions from the most renowned music professionals coming from all over Europe.

The 20 selected projects cover a great variety of urgent needs faced within the music sector. They will:

help fostering social change with projects focusing on diversity in the industry, more fairness and transparency as well as raising awareness on timely issues.
enhance technological development with projects using blockchain, immersive sound and VR and AR.
build bridges between different key actors of the ecosystem.

Download the entire JUMP press release.

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 open collaboration approach allows to power trustworthy, ethical AI systems like our Listen Local that we started out from Slovakia with the support of the Slovak Arts Council.

JUMP fellows building bridges between different key actors of the ecosystem.

Apart from our Demo Music Observatory the build bridges section Groovly with Martin Zenzerovich, From Play To Rec by Jeremy Dunne, Hajde Radio by Thibaut Boudaud, LowDee by Alex Davidson and ONO-HU! by Gina Akers.

Meet all the JUMP 2021 Fellows, including the technology and social change professionals!

Reprex is a start-up company based in the Netherlands and the United States that validated its early products in the Yes!Delft AI+Blockchain Lab in the Hague. In 2021 we joined the Dutch AI Coalition – NL AIC and requested membership in the European AI Alliance. Reprex is committed to applying reproducible in an open collaboration with our business, scientific, policy and civil society partners, and facilitate the use of open data and open-source software. Many fellows in the program are connected to other regions, like North America and Australia – because music is one of the most globalized industries and forms of art in the world! Reprex is a startup based in the Netherlands and the United States, and we are very excited to collaborate with our peers in new European territories, and in Canada and Australia.

Hope to meet you in these great events - maybe not only online!

Further links:

From Play to Rec on Facebook
HAJDE FR/EN

Follow up:

Jumping Ahead With the Digital Music Observatory (2021.11.13.)

Music Streaming: Is It a Level Playing Field?

Tue, 23 Feb 2021 21:23:00 +0200

Our article, Music Streaming: Is It a Level Playing Field? is published in the February 2021 issue of CPI Antitrust Chronicle, which is fully devoted to competition policy issues in the music industry.

The dramatic growth of music streaming over recent years is potentially very positive. Streaming provides consumers with low cost, easy access to a wide range of music, while it provides music creators with low cost, easy access to a potentially wide audience. But many creators are unhappy about the major streaming platforms. They consider that they act in an unfair way, create an unlevel playing field and threaten long-term creativity in the music industry.

Our paper describes and assesses the basis for one element of these concerns, competition between recordings on streaming platforms. We argue that fair competition 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.

You can read the entire issue and the full text of our article on Competition Policy International in pdf.

Music Streaming: Is It a Level Playing Field?

Tue, 23 Feb 2021 11:00:00 +0000

You can read the entire issue and the full text of our article on Competition Policy International in pdf.

Daniel Antal, co-founder of Reprex Was Selected into the 2021 Fellowship Program of the European Music Market Accelerator

Mon, 22 Feb 2021 21:23:00 +0200

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.

Our Demo Music Observatory is a demo version of the European Music Observatory based on open data, open source, automated research in open collaboration with music stakeholders. We hope that we can further develop our business model and find new users, and help the recovery of the festival and live music segment.
Listen Local is our AI system that validated third party music AI, such as Spotify’s or YouTube’s recommendation systems, and provides trustworthy, accountable, transparent alternatives for the European music industry. We hope to expand our pilot project from Slovakia to several European countries in 2021.

Reprex is committed to applying reproducible in an open collaboration with our business, scientific, policy and civil society partners, and facilitate the use of open data and open-source software.

Reprex Joins The Dutch AI Coalition

Tue, 16 Feb 2021 17:10:00 +0200

Reprex, our start-up, is based in the Netherlands and the United States that validated its early products in the Yes!Delft AI+Blockchain Lab in the Hague. In 2021, we decided to join the Dutch AI Coalition – NL AIC.

The NL AIC is a public-private partnership in which the government, the business sector, educational and research institutions, as well as civil society organisations collaborate to accelerate and connect AI developments and initiatives. The ambition is to position the Netherlands at the forefront of knowledge and application of AI for prosperity and well-being. We are continually doing so with due observance of both the Dutch and European standards and values. The NL AIC functions as the catalyst for AI applications in our country.

We are particularly looking forward to participating in the Culture working group of NLAIC, but we will also take a look at the Security, Peace and Justice and the Energy and Sustainability working groups. Reprex is committed to use and further develop AI solutions that fulfil the requirements of trustworthy AI, a human-centric, ethical, and accountable use of artificial intelligence. We are committed to develop our data platforms, or automated data observatories, and our Listen Local system in this manner. Furthermore, we are involved in various scientific collaborations that are researching ideas on future regulation of copyright and fair competition with respect to AI algorithms.

We are committed to applying reproducible in an open collaboration with our business, scientific, policy and civil society partners, and facilitate the use of open data and open-source software.

Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

Sat, 13 Feb 2021 18:10:00 +0200

The majority of music sales in the world is driven by AI-algorithm powered robots that create personalized playlists, recommendations and help programming radio music streams or festival lineups. It is critically important that an artist’s work is documented, described in a way that the algorithm can work with it.

In our research paper – soon to be published – made for the Listen Local Initiative we found that 15% of Dutch, Estonian, Hungarian, or Slovak artists had no chance to be recommended, and they usually end up on Forgetify, an app that lists never-played songs of Spotify. In another project with rights management organizations, we found that about half of the rightsholders are at risk of not getting all their royalties from the platforms because of poor documentation.

But how come that distributors give streaming platforms songs that are not properly documented? What sort of information is missing for the European repertoire’s visibility? Reprex is exploring this problem in a practical cooperation with SOZA, the Slovak Performing and Mechanical Rights Society, and in an academic cooperation that involves leading researchers in the field. A manuscript co-authored Martin Senftleben, director of the Institute for Information Law in Amsterdam, and eminent researchers in copyright law and music economics, Reprex’s co-founder makes the case that Europe must invest public money to resolve this problem, because in the current scenario, the documentation costs of a song exceed the expected income from streaming platforms.

In the European Strategy for Data, the European Commission highlighted the EU’s ambition to acquire a leading role in the data economy. At the same time, the Commission conceded that the EU would have to increase its pools of quality data available for use and re-use. In the creative industries, this need for enhanced data quality and interoperability is particularly strong. Without data improvement, unprecedented opportunities for monetising the wide variety of EU creative and making this content available for new technologies, such as artificial intelligence training systems, will most probably be lost. The problem has a worldwide dimension. 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. A trade-off between data harmonisation and interoperability on the one hand, and transparency and accountability of content recommender systems on the other, could pave the way for successful new initiatives. Download the manuscript from SSRN

Our Slovak Demo Music Database project is a best example for this. We started systematically collect publicly available information from Slovak artists (in our write-in process) and ask them to give GDPR-protected further data (in our opt-in process) to create a comprehensive database that can help recommendation engines as well as market-targeting or educational AI apps.

We believe that one of the problems of current AI algorithms that they solely or almost only work with English language documentation, putting other, particularly small language repertoires at risk of being buried below well-documented music mainly arriving from the United States.

We are looking for rightsholders and their organizations, artists, researchers to work with us to find out how we can increase the visibility of European music.

Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

Sat, 13 Feb 2021 11:00:00 +0000

This article, published in JIPITEC in 2022, remains one of our most cited works on copyright, metadata, and cultural policy.

The paper shows how fragmented copyright metadata undermines the visibility of European creative works, causes royalty losses for artists, and limits the ability of European industries to compete globally in emerging areas like AI training and recommender systems.

Using the music industry as a central case study, the article highlights why improved metadata and licensing infrastructures are vital. Its findings directly connect to our current projects on trustworthy AI, cultural data spaces, and fair remuneration systems.

📄 Read the published version in JIPITEC: Full text PDF
📄 Preprint version available on SSRN: SSRN abstract

Feasibility Study On Promoting Slovak Music In Slovakia & Abroad

Sun, 27 Dec 2020 11:00:00 +0000

Download the study in Slovak or in English.

In 2015, realizing the low visibility and income-generating potential of Slovak music, the legislation introduced an amendment to the broadcasting act to regulate local content in radiostreams. The Slovak content promoting policy was well-intended but not based on any impact assessment, and it reached its goal only partially.

The Slovak broadcasting quotas in comparison with other national quotas a very simple, and they are impossible to measure, which makes both compliance and enforcement very difficult. Radio editors do not get any help to find music that fits into the playlists and fulfil the quota obligations – in many cases, it is impossible for them to find out if a song actually meets the quota requirements. For the same reason, neither is enforcement possible.

Another deficiency of the broadcasting quotas is that because of its fuzzy target, it is not clear whom it tries to help, and it has few friends. It is unclear how performers, composers or Slovak music producers can benefit from the system. Furthermore, it only helps a few genres, and it decreases the chances of other Slovak music in instrumental and non-Slovak language genres (for example, classical, jazz, rock) to be heard.

And at last, radio is losing its importance in music discovery. New generation find the music during their music discovery age on YouTube and digital streaming platforms. A Slovak content promoting policy that does not work on digital streaming platforms will be obsolete when radio content providers will switch to digital streaming in the foreseeable future.

Our Feasibility Study follows the following logic: In the first chapter we introduce various music recommendation systems in the context of local content promotion polices, like local mandatory content quota regulations.

In the second chapter, we consider the market-based or creative industry economy supporting policy goals, measurements, and potential support given to artists and producers.

We then turn in the third chapter to content-based local regulations promoting the use of the Slovak language or Slovak music content, irrespective of the performers and producers nationality, residence or ethnicity.

We introduce the idea of the Slovak Music Database, a comprehensive, mainly opt-in, opt-out database that of Slovak artists and Slovak music that should be supported by the local content regulation and other policies. We also create a Demo Slovak Music Database to understand the problem and scope of the creation of the comprehensive version.

The project website contains the Demo Slovak Music Database.

We also created a Demo Recommendation System. We explain here why.

Research questions

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?
How can we measure the value gap between what some media platforms, most particularly the biggest YouTube, does not pay out to the Slovak stakeholders within Slovakia?
What is the interplay of the various definitions on market share and national quota targets?
How ‘shadow-markets’ of home copying and unlicensed media platforms, such as YouTube impact market shares directly and national quotas indirectly?
How can modern data science, predictive microeconomics and statistics help increase the market share of Slovak music in Slovakia and abroad?

Thanks for the entire Reprex team who contributed to the English version:

Dr. Emily H. Clarke, musicology
Stef Koenis, musicologist, musician
Dr. Andrés Garcia Molina, data scientist, musicologist, editor
Kátya Nagy, music journalist, research assistant;

and the Slovak version:

Dominika Semaňáková, musicologist, editor
Dáša Bulíková, musician, translator.

Demo Slovak Music Database

Thu, 17 Dec 2020 17:10:00 +0200

We are finalizing our first local recommendation system, Listen Local Slovakia, and the accompanying Demo Slovak Music Database. Our aim is

Show how the Slovak repertoire is seen by media and streaming platforms
What are the possibilities to give greater visibility to the Slovak repertoire in radio and streaming platforms
What are the specific problems why certain artists and music is almost invisible.

In the next year, we would like to create a modern, comprehensive national music database that serves music promotion in radio, streaming, live music within Slovakia and abroad.

To train our locally relevant, alternative recommendation system, we filled the Demo Slovak Music Database from two sources. In the opt-in process we asked artists to participate in Listen Local, and we selected those artists who opted in from Slovakia, or whose language is Slovak. In the write-in process we collected publicly available data from other artists that our musicology team considered to be Slovak, mainly on the basis of their language use, residence, and other public biographical information. The following artists form the basis of our experiment. (If you want to be excluded from the write-in list, write to us, or you want to be included, please, fill out this form.)

Click here to view the table on a separate page

Modern recommendation systems usually rely on data provided by artists or their representatives, data on who and how is listening to their music, and what music is listened to by the audience of the artists, and certain musicological features of the music. Usually they collect data from various data sources, but these data sources are mainly English language sources.

The problem with these recommendation systems is that they do not help music discovery, and make starting new acts very difficult. Recommendation systems tend to help already established artists, and artists whose work is well described in the English language.

Our alternative recommendation system is a utility-based system that gives a user-defined priority to artists released in Slovakia, or artists identified as Slovak, or both. The system can be extended for lyrics language priorities, too. Currently, our app is demonstration to provide a more comprehensive database-driven tool that can support various music discovery, recommendation or music export tools. Our Feasibility Study to build such tools and our Demo App is currently under consultation with Slovak stakeholders.

Listen Local is developing transparent algorithms and open source solutions to find new audiences for independent music. We want to correct the injustice and inherent bias of market leading big data algorithms. If you want your music and audience to be analysed in Listen Local, fill this form in. We will include you in our demo application for local music recommendations and our analysis to be revealed in December.

Listen Local: Why We Need Alternative Recommendation Systems

Mon, 14 Dec 2020 17:10:00 +0200

The first version of our demo application

Recommendation systems utilize knowledge about music content and their audiences while also pursuing the objectives or needs of recommenders.

The simplest recommendation systems just follow the charts: for example, they select from well-known current or perennial greatest hits. Such a system may work well for an amateur DJ in a home party or a small local radio that just wants to make sure that the music in its programme will be liked by many people. They reinforce existing trends and make already popular songs and their creators even more popular.

If the recommendation engine is supported by big data and a machine learning system – or increasingly, a combination of several machine learning algorithms – the general modus operandi is to exploit information about both content and users in order to achieve certain goals.

How algorithmic recommendation systems work?

How recommendation systems work?

Spotify’s recommendation system is a mix of content- and collaborative filtering that exploits information about users’ past behaviour (e.g. liked, skipped, and re-listened songs), the behaviour of similar users, as well as data collected from the users’ social media and other online activities, or from blogs. Deezer uses a similar system that is boosted by the acquisition of Last.fm – big data created from user comments are used to understand the mood of the songs, for example.

Spotify makes 16 billion music recommendations each month in 2020.

YouTube, which plays an even larger role in music discovery, uses a system comprised of two neural networks: one for candidate generation and one for ranking. The candidate generation deep neural network provides works on the basis of collaborative filtering, while the ranking system is based on content-based filtering and a form of utility ranking that takes into consideration the user’s languages, for example.

What makes these systems common is that they maximize the algorithm creators’ corporate key performance indicators. Spotify wants to be ‘your playlist to life’ and increase the amount of music played during work or sports in the background, during travelling, or active music listening –- i.e. maximizing the number of hours spent using it, and do not let empty timeslots for other music providers, such as radio stations. YouTube and Netflix have similar targets. They are in many ways like commercial radio targets, which want to maximize the time spent listening to the broadcast stream. Radios and YouTube, in particular, have similar goals because they are mainly financed through advertising. For Spotify or Netflix, their key financial motivation is to avoid users’ cancelling their subscriptions or changing it to different providers, such as Amzon, Apple or Deezer.

What is the problem with black box recommendation systems?

What they also have in common is that they do not aim to give a fair chance to each uploaded song, serve equally every artist, or provide whatever equality of chances for English, Slovak or Farsi language content.

They tend to reinforce trends similarly to music charts, but with far bigger efficiency. As the Dutch comedian, author and journalist Arjen Lubach explains the YouTube algorithm, to keep their personal recommendations engaging all the day and all of the night, they create a comfortable universe for the user allows little distraction in. If the user wants to listen to global hit music, or stoner rock, it will never be distracted with anything else.

Zondag met Lubach on Dutch public broadcaster VPRO. Click settings sign to change the language of the captions.

The problem with such hyper-personalized media is that they leave no room for public activities. Public broadcasters, which had a monopoly to television broadcasting in most European countries until the early 1990s, for example, were aiming to air a diversity of news, knowledge and access to local culture. Many countries on all continents have maintained local content guidelines for broadcasting on public, commercial and community television and radio channels, for example, local music and films, and reliable news as a public service. Personalized media-, social media- and streaming platforms do not have such obligations.

Black box recommendation systems usually maximize a corporate key performance indicator, and they are not subject to usual public new service or local content regulations that traditional broadcast media is.
The goal and the steps that the algorithm is pursuing is not know to content creators, and they do not know when will the algorithm work for their benefit or against them.

Transparent and regulated AI

In our view, utility-based recommendation system can provide a bridge between current, corporate-owned systems that maximize a media or streaming platforms’ business indicators.

Public new service requirements or local content requirements (“national quotas”) set for commercial broadcasting are similar to utility or knowledge-based recommendation systems. A utility-based recommendation system, for example, would prefer from two candidates for a playlist the one that has a Slovak composer, or a performer from Wales, or which has Farsi lyrics.

Our Demo App creates recommendations on the basis of a pre-existing radio or personal streaming playlist that, by choice, contains a pre-defined ratio of music produced in Slovakia, or performed by Slovak artists. We will soon add Dutch and Hungarian choices to this demo, but naturally, we could add any city’s, regions’, province’s our countries preferences into the app.

Our Demo App and accompanying Feasibility Study in Slovakia shows how can a regulator create better broadcasting regulations, learning from the experience with AI-driven streaming platforms, and how can it apply the goals of local content requirements (such as a certain visibility for Slovak or a city-based music) and public service requirements (for example, spreading reliable information or stopping hateful music.)

Access for all

We do not believe that the current heated discussion on the re-regulation of AI and music streaming will solve all the problems of independent artists, bands from ethnic or racial minorities, or otherwise vulnerable producers. New regulation can limit the unintended collateral damage of big data algorithms deployed by big corporations, but they will not bring down the benefits of AI to these creators.

Take the example of the controversial new initiative that let’s artists, labels and publishers to promote their music in exchange for a cut in royalty rates. While felt by many artists injust and even corrupt, it is an answer to the growing need to influence how the recommendation algorithms are promoting certain music at the expense of tens of millions of sound recordings that are not recommended.

We believe that algorithms create value for the users, and if artists are not happy to pay for corporations to influence black box algoritms, thatn they must collaborate and share data, and build large enough data pools so that they can deploy white, transparent algorithms that work for them.

Our Feasibility Study shows why it is important that creators have a control over what data describes their music, their biographies and other information online, because corporate streaming platforms use this information for their algorithms.
We should that with relatively little effort creators can pool enough information to create alternative recommendation systems that follow a more agreeable goal, that is sensitive to local content requirements and more access to new artists, women or black performers, or which suppress hateful lyrics.
The benefit of an open algorithm and pooled data is that artists can actively look for audiences in various age groups or in cities that are accessible for them on a performing tour after the pandemic.

Overall, we want to show that 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.

Reproducible research in practice: empirical study on the structural conditions of book piracy in global and European academia

Sat, 05 Dec 2020 08:10:00 +0200

PLOS One is the fourth most influential multidisciplinary journal after Nature, and Science, and Proceedings of the National Academy of Sciences of the United States of America (based on H index.) On December 3, 2020 it published a paper co-authored by Dr. Balazs Bodo, associate professor at the Institute for Information Law (IViR), Daniel Antal (Reprex, Demo Music Observatory), a data scientist interested in reproducible research, as an independent researcher, and Zoltan Puha, a Data Science PhD at Tilburg University, JADS. PLOS (Public Library of Science) is a nonprofit Open Access publisher, empowering researchers to accelerate progress in science and medicine by leading a transformation in research communication.

The article utilizes the our reproducible datasets created with our regions package, and builds on many years of expertise in empirical research on the field of music and audiovisual piracy, home copying and private copying compensation (see for example Private Copying in Croatia.) Our aim is to provide reliable, high quality indicators for the creative industries not only on national, but provincial, state, regional and metropolitan area level, too, because these levels are often more relevant for creators, performers and policy-makers.

The topic of the paper is Library Genesis (LG), the biggest piratical scholarly library on the internet, which provides copyright infringing access to more than 2.5 million scientific monographs, edited volumes, and textbooks. The paper uses advanced statistical methods to explain why researchers around the globe use copyright infringing knowledge resources. The analysis is based on a huge usage dataset from LG, as well as data from the World Bank, Eurostat, and Eurobarometer, to identify the role of macroeconomic factors, such as R&D and higher education spending, GDP, researcher density in scholarly copyright infringing activities.

We created a global and a far more detailed European model for pirate book downloads.

The main finding of the paper is that open access, even if it is radical, is not a panacea. The hypothesis of the research was that researchers in low-income regions use piratical open knowledge resources relatively more to compensate for the limitations of their legal access infrastructures. The authors found evidence to the contrary. Researchers in high income countries and European regions with access to high quality knowledge infrastructures, and high levels of funding use radical open access resources more intensively than researchers in lower income countries and regions, with less resourceful libraries. This means that while open knowledge is an important resource to close the knowledge gap between centrum and periphery, equality in access does not translate into equality in use. Structural knowledge inequalities are both present and are being reproduced in the context of open access resources.

The paper is unique not just because of the data it is based on. It also sets new standards in interdisciplinary legal research by publishing the paper, the data and the software code in the same time in open access repositories, following reproducible research best practices — the practices that we want to promote in our Demo Music Observatory (later renamed: Digital Music Observatory) and further data observatories to serve business, evidence-based policy and scientific research.

Can scholarly pirate libraries bridge the knowledge access gap? An empirical study on the structural conditions of book piracy in global and European academia

Thu, 03 Dec 2020 11:00:00 +0000

We created a global and a far more detailed European model for pirate book downloads.

The paper is unique not just because of the data it is based on. It also sets new standards in interdisciplinary legal research by publishing the paper, the data and the software code in the same time in open access repositories, following reproducible research best practices — the practices that we want to promote in our Digital Music Observatory and further data observatories to serve business, evidence-based policy and scientific research.

Our research was funded from the Horizon Europe 2020 Research grant #710722 “OPENing UP new methods, indicators and tools for peer review, dissemination of research results, and impact measurement”.

Feasibility Study For The Establishment Of A European Music Observatory & The Demo Observatory

Mon, 16 Nov 2020 07:03:00 +0200

The Feasibility study for the establishment of a European Music Observatory was published on 13 November. Our private observatory, CEEMID was consulted in the creation of the Feasibility Study, and some of our recommendations found way into the consultant’s document. We created a Demo Music Observatory to provide a practical guidance on the decisions facing the European stakeholders, and to answer the questions that were left open in the Feasibility Study — particularly on data integration and the institutional model, where a wrong choice can lead to very long delivery time, quality control and budgeting.

We have been developing our Demo Music Observatory in the world’s 2nd ranked university-backed incubator program, the Yes!Delft AI Validation Lab since 15 September 2020. Our aim is to show a better organizational model, examples of research automation and other data integration innovation that can reduce the budgetary needs of the European Music Observatory by 80-90% and provide far more timely, accurate, and relevant service than most data observatories in Europe.

CEEMID has been creating a similar data observatory to the foreseen European Data Observatory, solely based on the contribution of about 60 European stakeholders. As the Feasibility Study suggests, we would be happy to transfer much of CEEMID’s content to the European Data Observatory, which could potentially fill up about 50-70% of the envisioned observatory. We are building our Demo Music Observatory based on the 2000 pan-European indicators collected by CEEMID since 2014.

Challenge Our Demo Observatory: Check out the Music Diversity & Circulation Pillar of our Demo Music Observatory. If you do not find what you are looking for, contact us — we will try to put the data there from our repositories.

Illusory data gap: active and music participation is available on EU level both for gender groups or four ethnic minorities – this is regularly featured in various European CAP surveys and in our national CAP surveys, too.

The Feasibility Study is based on perceived data gaps between data needs of the European stakeholders and data availability. We have shown earlier this year to the European stakeholders that much of these data gaps are illusory. We would like to give about 50 indicators with full documentation, automated, weekly, monthly, quarterly, or annual refreshment for free for all music industry users. We would like to challenge the stakeholders to formulate data requests to us and think together on the ways how could the European music industry build a better observatory faster and with less cost.

Challenge Our Demo Observatory: Check out the Music Economy Pillar of our Demo Music Observatory. If you do not find what you are looking for, contact us — we will try to put the data there from our repositories.

The Feasibility Study concludes that a “European Music Observatory would require a very significant allocation of funds, beyond what could be currently expected from the possible budget of the future Creative Europe programme”. While the Feasibility Study provide cost options, or any cost-benefit analysis, we are certain that this is an exaggeration. Most European data observatories operate with an annual 20,000-200,000-euro subsidy. We want to show with our Demo Music Observatory what can be achieved with an annual budget of 20,000 euros, 50,000 euros, 100,000 euros or 200,000 euros.

Challenge Our Demo Observatory: Check out the Music, Society and Citizenship Pillar of our Demo Music Observatory. If you do not find what you are looking for, contact us — we will try to put the data there from our repositories.

Launching Our Demo Music Observatory

Tue, 15 Sep 2020 08:00:39 +0000

Today, on 15 September 2020, we officially launched our minimal viable product as we promised to partners back in February. This was a particularly difficult period for everybody. We aspired to deliver by September in a very different environment, our hopes for commissioned work went up in flames with the pandemic, and our targeted users, musicians and music entrepreneurs, talent managers, music venues lost most of their income. The organizations helping them, granting authorities, export offices and collective management societies are overwhelmed with the problem. During these troublesome times, our team expanded, attracted great new talent, and kept working.

Our first product is the Demo Music Observatory, a collaborative, automated research-based observatory for the music industry, one that is particularly hard hit by the COVID19 crisis. Not only great artists, composers, technicians, managers fell victim to the virus, but musicians lost about 50–90% of their income from live music. This translates to a 100% loss for the live music technicians and managers.

See our earlier blogpost on what you see on the video.

The music industry was never a place for great job security. For putting up a show, you usually need a network of 10–200 artists, technicians and managers to work together as freelancers without all those social benefits that many people enjoy in other walks of life. We have been trying to figure out how to help this microenterprise and freelancer-network based industry with research for five years. Our aim is to make them competitive when they are talking with their buyers: Google, Apple, Spotify, who are really heavy-weight data and AI pros. Our better plan their tours, when they will be back on the road, to understand what sort of audiences and purchasing power waits for them in different European cities.

We are launching at a time when the music industry is crying for help.Therefore, we have decided to make our demo observatory open and unfinished. Over the last 7 years, we have built up about 2000 music and creative sector indicators to be used for business KPIs, forecasting targets, grant evaluations, royalty valuations, concert demography target group analysis and other professional uses. We would like to open up, based on your needs, about 50 well-designed indicators, and pledge to keep it daily refreshed, corrected, documented, citaable, downloadable. Also, feel free to use our most valuable source code—use it for your own purposes, even modify it, as long as you keep it open.

For our smaller partners, we follow what musicians do these days on Bandcamp: name your price. We make a pledge to our small partners: if you need reliable data to plan your next grant calls, calculate royalties, compensations, predict hit candidates, give us the job—and name your price. Post-corona, you can take for a dollar the best music from Bandcamp. You can take our research products, for a limited period, for any amount you name, as long as it is for a good cause and serves the industry, musicians, technicians or managers. In return, we ask for your feedback. Help us validate whether we are on the right track, tell us how we can cooperate after the pandemic, in better times.

Our larger and better funded partners? We ask you to pay the price we name, because we believe that it is a well-justified, fair and competitive price, set by pricing experts.

We appreciate it if you take a look at our offering, or if you pass this blogpost on to your colleagues in the industry. Our main target audience initially are music professional in broader Europe, but we are planning to cover all major global markets very soon, too. Feedback from the U.S., Australia, Canada, Colombia, Brazil & Argentina is particularly welcome as we have great plans over there!

Who we are?

We started our operations on 1 September 2020 on the basis of CEEMID, a pan-European data observatory that created about 2000 music and creative industry indicators for its users. In the coming days, we are gradually opening up about 50 music industry and 50 broader creative industry indicators in a fully reproducible workflow, with daily re-freshed, re-processed, well-formatted and documented indicators for business and policy decisions.

We would like to validate this approach in one of the world’s most prestigious university-backed incubator programs, in the Yes!Delft AI/Blockchain Validation Lab. We’re finalist on their selection, and all help before 23 September from our friends in the music industry is more than appreciated. If we get there, we can rely on probably the best pros in Europe to make our offering better tailored and financially sustainable.

Get in touch!

We use the very simple and extremely secure keybase.io, a kind of mix of Whatsapp, Skype, Google Drive, One Drive and zoom. You can get in touch on that platform with us in anytime here.

You can easily contact on LinkedIn Daniel or Kátya and of course, we have a usually working email contact form, too. Our email is name.surname at our main domain.

Video credits

Data acquisition and processing: Daniel Antal, CFA and Marta Kołczyńska, PhD (survey data).
Documentation automation: Sandor Budai
Video art: Line Matson
Music: Moon Moon Moon.

Creating An Automated Data Observatory

Fri, 11 Sep 2020 16:00:39 +0000

We are building data ecosystems, so called observatories, where scientific, business, policy and civic users can find factual information, data, evidence for their domain. Our open source, open data, open collaboration approach allows to connect various open and proprietary data sources, and our reproducible research workflows allow us to automate data collection, processing, publication, documentation and presentation.

Our scripts are checking data sources, such as Eurostat’s Eurobase, Spotify’s API and other music industry sources every day for new information, and process any data corrections or new disclosure, interpolate, backcast or forecast missing values, make currency translations and unit conversions. This is shown illustrated with an earlier post.

For direct access to the file visit this link.

In the video we show automated the creation of an observatory website with well-formatted, statistical data dissemination, a technical document in PDF and an ebook can be automated. In our view, our technology is particularly useful technology in business and scientific researech projects, where it is important that always the most timely and correct data is being analyzed, and remains automatically documented and cited. We are ready deploy public, collaborative, or private data observatories in short time.

Data processing costs can be as high as 80% for any in-house AI deployment project. We work mainly with organization that do not have in house data science team, and acquire their data anyway from outside the organization. In their case, this rate can be as high as 95%, meaning that getting and processing the data for deploying AI can be 20x more expensive than the AI solution itself.

AI solutions require a large amount of standardized, well processed data to learn from. We want to radically decrease the cost of data acquisition and processing for our users so that exploiting AI becomes in their reach. This is particularly important in one of our target industries, the music industries, where most of the global sales is algorithmic and AI-driven. Artists, bands, small labels, publishers, even small country national associations cannot remain competitive if they cannot participate in this technological revolution.

We would like to validate this approach in one of the world’s most prestigious university-backed incubator programs, in the Yes!Delft AI/Blockchain Validation Lab.

Video credits

Data acquisition and processing: Daniel Antal, CFA and Marta Kołczyńska, PhD (survey data).
Documentation automation: Sandor Budai
Video art: Line Matson
Music: Moon Moon Moon.

Central & Eastern European Music Industry Report 2020

Wed, 29 Jan 2020 16:00:00 +0000

CEEMID & Consolidated Independent presented and discussed with stakeholders the Central & Eastern European Music Industry Report 2020 as a case-study on national and comparative evidence-based policymaking in the cultural and creative sector on the CCS Ecosystems: FLIPPING THE ODDS Conference – a two-day high-level stakeholder event jointly organized by Geothe-Institute and the DG Education and Culture of the European Commission with the Creative FLIP project.

The CEE Report builds on the results of the first Hungarian, Slovak, Croatian and Czech music industry reports are compared with Armenian, Austrian, Bulgarian, Lithuanian, Serbian and Slovenian data and findings.

Our research findings were earlier presented and discussed in Vienna, Prague, Budapest and Bratislava with stakeholders.

You can find the earlier presentations in the blog section of the website.

Executive Summary

The first Central European Music Industry Report is the result of a co-operation that started among stakeholders in three EU countries five years ago to measure the economic value added of music – the basis of a modern royalty pricing system. This gave birth to CEEMID, originally the Central & Eastern European Music Industry Databases, a data integration programme that now in 2020, covers all of Europe. CEEMID fulfils similar roles to the planned European Music Observatory and supports all pillars of the future pan-European system.

The comparison of Western and Eastern music audiences reveals key demographic differences that make the unchanged adoption of business practices from mature markets in the region questionable. Chapter 2 of this report will show these differences and their consequences on music markets, in terms of visiting and acquisition likelihood, frequency, seasonality and purchasing capacity. This is an example of how CEEMID fulfils the role of Pillar 3 (music, society and citizenship) in the planned European Music Observatory.

Chapter 3 contrasts market demand with the supply strategies of musicians. CEEMID has been surveying music professionals, including artists, technicians and managers about their working conditions, market conditions and plans for five years across a growing number of countries. In 2019 we invited 100 national and regional stakeholders to distribute our surveys. In some countries, our surveys already have several years of historic data, making the resulting musician database probably the largest ever source of data about how music is produced and how musicians live. We are constantly looking for partners to roll out this survey to new countries in new languages.

The CEE region has comparative advantages in big music events like festivals, and it has become one of the most important hubs for cultural tourism in the world. We explain this phenomenon in Chapter 4 by showing the differences in demand composition, demography and supply of venues in the second chapter. The lack of a modern and dense network of permanent music venues gave rise to magnificent music festivals in the CEE. Open’er, Sziget and Exit are among the biggest and best festivals in the world, closely followed by several smaller festivals in all countries. The share of festivals in the live music market is many times higher than in Western Europe and they provide vital export revenues to the local music economies. However, they play a limited role in finding new audiences for local artists, as they are increasingly programming for Western audiences by providing shows of international hits. They can only very partially fill in the gaps left by the small venue problem that hit the emerging markets harder than the UK or Australia, where policy action had been already taken to reverse the decline of the availability of smaller live music venues.

On the recording side, our analysis shows that modern digital services are growing at a faster rate than in mature markets. Because of lower repertoire competition, streaming quantities are similar for a typical Austrian, Czech, Hungarian, Polish or Slovak track than in the mature markets. However, revenue growth is limited because of the interplay of several analysed factors. Our analysis of the live and recorded music markets shows that CEEMID fulfils the roles of the Pillar 1 (music economy) of the planned European Music Observatory.

Most recorded music sales revenue in the region comes from streaming platforms, just like in the mature markets. Successful sales strategies require a solid knowledge of the global marketplace and the ability to understand and train sales algorithms. Micro-enterprises, such as independent labels, have very limited ability to cope with these functions, given that they do not have market research or R&D functions. CEEMID and Consolidated Independent have started initiating open, national R&D consortia to create the necessary concentration in data assets, analytical capacity and budgets to close this gap. As a first step, CEEMID and Consolidated Independent have created a large, independent music dataset based on hundreds of millions of royalty statement entries to create our market indexes, styled after stock market and bond market indexes. Streaming opportunities are fast changing as roll-out of streaming services is happening at a different rate in various territories; subscription charges and the exchange rate to the producer’s currency vary and repertoire competition emerges in the market. Our volume and revenue indexes in Chapter 5.3 are aimed at creating sales algorithms that optimize sales volumes and expected revenues. We believe that this analysis also reveals that CEEMID partially fulfils the roles of Pillar 2 (music diversity and circulation) and feeds important data into Pillar 4 (innovation).

The region has far bigger untapped potential than most music business executives believe. Households in the region spend a significantly lower share of their recreational budget on music than their Western, Southern or Nordic peers. The region has a lot of untapped cultural purchasing power because servicing is particularly challenging in both the live and recorded sides of the business.

This upside potential cannot be tapped without better pricing. Royalty levels are often very low in the region. Due to many combined effects analysed in this short report, the gap between royalties earned in the CEE and Western Europe is several times bigger than the difference in GDP or national average wage. These gaps are partly caused by special interests preventing collective management from charging appropriate tariffs for restaurants, media companies or electronic appliance importers and manufacturers, and partly by unfavourable taxation of cultural products and services.

CEEMID was designed to create economic evidence on royalty pricing, private copying compensation and the creation of economic value added in the industry. In the first Hungarian Music Industry Report of ProArt and in the first Slovak Music Industry Report we have shown that economic and taxation policies of the CEE countries aimed to support car and electronics manufacturing create a distorted, unfavourable economic regime for creative industries. We want to help local stakeholders with economic evidence to correct these discriminatory policies during the overhaul of the EU VAT system. We have been helping various national organizations with economic evidence, presented in the light of latest EU jurisprudence, to improve their pricing activities. Our thousands of indicators were also used in ex ante evaluations of granting schemes.

In 2020, all EU member states will change their copyright administration legislation because of the national implementations of the 2019/790 Digital Single Market directive. CEEMID provides evidence in several countries about the size and impact mechanism of the value transfer, and generally the widespread use of the copyright exemption for private copying. We believe that the thousands of pan-European music industry indicators that we have aggregated over the five years will play a vital role in these regulatory processes.

CEEMID fulfils its roles with a very thorough exploitation of the EU’s 17-years-old Open Data regime with the re-use of public sector information, and a very careful mapping of the music industry. These maps help us conduct annual surveys among musicians and the audience, and they help us connect (always with pre-approval and with a user mandate) to industry databases. We do not only cover the EU countries, but increasingly (potential) candidate countries and neighbourhood countries.

In our vision, this data collection and integration, i.e. Pillars 1-3 should be available for all music stakeholders, should remain public and publicly funded. The last Pillar of the observatory, innovation, is where private entities should compete. The founders of CEEMID and Consolidated Independent believe that this report demonstrates the business and policy benefits of such a system with the analysis of the Central & Eastern European music markets. We believe that this way CEEMID is in a position to serve most of the planned functions of the envisioned European Music Observatory, and we are looking for ways to make either our thousands of indicators, or our data collection and integration software open source and available for all stakeholders in the EU and its neighbours. CEEMID was born out of necessity to level out the different levels of public research and statistical coverage of the EU member states. In our view, private entities in the future should focus their investments in Pillar 4 of the planned observatory, i.e. competing in innovation with creating new models, algorithms and services based on data that is available throughout the European Union without giving further advantage to the already mature markets.

Daniel Antal | Antal Dániel honlapja

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