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Mirex

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.

Bloomen

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.

Bloomen

Kapsoulis, Nikolaos; Psychas, Alexandros; Palaiokrassas, Georgios; Marinakis, Achilleas; Litke, Antonios; Varvarigou, Theodora; Bouchlis, Charalampos; Raouzaiou, Amaryllis; Calvo, Gonçal; Escudero Subirana, Jordi. 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“.

3^rd 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 23_^rd International Conference on Digital Audio Effects (DAFx-20), volume 5, pages 273-280.

Meléndez-Catalán, B. (2021) “Relative Music Loudness Estimation in TV Broadcast Audio Using Deep Learning: An industrial perspective”.

Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.

G. Cortès, Álex Ciurana, Emilio Molina, Marius Miron, Owen Meyers, Joren Six and Xavier Serra. “BAF: An Audio Fingerprinting Dataset For Broadcast Monitoring”.

In Proc. of the 23rd Int. Soc. for Music Information Retrieval Conf. (ISMIR), Bangalore, India, 2022 [dataset, Github]

Covers

F. Yesiler, E. Molina, J. Serrà, and E. Gómez., “Investigating the efficacy of music version retrieval systems for setlist identification”, in Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021), 2021.

LoudSense

Roser Batlle-Roca, Perfecto Herrera-Boyer, Blai Meléndez, Emilio Molina and Xavier Serra. “Towards a Characterization of Background Music Audibility in Broadcasted TV” in International Journal of Environmental Research and Public Health, Special Issue Speech Communication in Complex Auditory Scenes and Effects on Voice Behaviour and Health, Listening Comfort, Well-Being, and Learning.

LETS

In 2023, we were recognised as one of 135 Leading European Tech Scale-ups (LETS) that are succeeding globally to stand out for our international reach and the sustainability of our business model.