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We’re the Operating System for the Music Industry. A platform that pumps up the jam of every play.

Over the last 15 years, we’ve grown from a tech hub, to a TV and radio music tracking company to a data company making sure artists get paid for every play. We’re currently helping over 4746 leading music companies to amplify the value of music, delivering 27 billion matches and 92 million identifications every month.

Yesterday’s papers

Yesterday’s papers


Summus is a Latin word that describes the greatness of things. In our case, it’s a summit we organise every year to honour the greatness of music.
It's a place where we meet with friends and share the lessons we learnt or discuss today’s challenges.

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In 2018, MIREX -the most important international competition of MIR (Music Information Retrieval) algorithms- recognised our music detection algorithm as the best in the field, with accuracy 10 percentage points higher than the algorithm in second place. In 2019, our first position was reestablished as we presented 2 new algorithms and they both obtained better results in comparison to the algorithm submitted in 2018.



BMAT was appointed as ‘Key Innovator’ by the European Commission’s Innovation Radar for the developments in the H2020 Bloomen project: Blockchain technology will be used to provide copyright protection of media content.



Kapsoulis, Nikolaos; Psychas, Alexandros; Palaiokrassas, Georgios; Marinakis, Achilleas; Litke, Antonios; Varvarigou, Theodora; Bouchlis, Charalampos; Raouzaiou, Amaryllis; Calvo, Gonçal; Escudero Subirana, Ordi. 2020. Consortium Blockhain Smart Contracts for Musical Rights Governance in a Collective Management Organizations (CMOs) Use Case

Future internet 12, no. 8: 134.


Industrial Doctorates

Meléndez-Catalán, B. Molina, E., and Gómez, E. (2017). BAT: an open-source web-based audio-events annotation tool“.

3rd Web Audio Conference

Meléndez-Catalán, B. Molina, E., and Gómez, E. (2019b). Open broadcast media audio from TV: A dataset of TV broadcast audio with relative music loudness annotations“.

Transactions of the International Society for Music Information Retrieval

Meléndex-Catalán, B. Molina, E., and Gómez, E. (2020). Relative music loudness estimation, using temporal convolutional networks and a CNN feature extraction front-end“.

In Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx-20), volume 5, pages 273-280.

Our Scientific Publicactions and Awards

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Get to know us better

Founded in 2006, BMAT is a music innovation company with a mission to index all music usage and ownership data.

We help all different companies in the music industry better their data operations to make sure artists get paid for their plays.

Every day we deliver 27 billion matches and 80 million identifications to CMOs, publishers, record labels, broadcasters and DSPs globally.