Innovation Lab
Empowering the music industry with ground-breaking audio technologies.
Innovation Lab
Intro
We’re obsessed with challenging ourselves to come up with better, faster, stronger – and more accurate – solutions for what we do. That’s why we invest over 3 million euros every year in our in-house Research and Development.
Our Proprietary Audio Technologies
We match the fingerprint of any audio against the 500 years worth of indexed music in our database. Our fingerprints maintain accurate performance in conditions of all types – interruptions, speech, sound effects, for example – by running on the selection of discriminative robust events. It is also used for copyrighting verification.
We match the metadata from digital sales providers with the internal music databases of collecting societies and enrich it with our own database to ensure it is complete. Every day we deliver 27 billion matches to make sure music creators receive the royalties they deserve.
Using deep learning tech, we scan audio recordings to detect cues that show the presence of music. For every 2-second segment, we output a value to represent both the musical and the simultaneous non-musical sounds present.
The audibility estimation determines whether the background music in a TV programme, show or advert is “audible” or “inaudible”. The specifications are given by the client in each case.
We use our well-trained algorithm to confirm whether any 2 audio files are the same musical work – when processed on a song vs song basis – or not.
An audio recording is processed and evaluated. A sequence of identified notes corresponding to the audio recording is determined by iteratively identifying potential notes within the audio recording. A rating for the audio recording is determined using a tuning rating and an expression rating. The audio recording includes a recording of at least a portion of a musical composition.
Methods and devices for estimating audibility of audio samples in audio mixes of broadcast media programs are proposed. Example methods comprise the steps of providing a representation matrix of an audio mix, the audio mix comprising the audio sample and additional audio, providing a representation matrix of the audio sample, subtracting the representation matrix of the audio sample from representation matrix of the audio mix to generate a difference matrix, applying an audibility model to the difference matrix to generate an audibility matrix, determining an audibility level for each element of the audibility matrix, and averaging the determined audibility levels of the matrix to estimate the audibility of the audio sample.
Our patents
Our Research And Innovation Projects
FuturePulse – Multimodal Predictive Analytics and Recommendation Services for the Music Industry – has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 761634
Bloomen – Blockchains in the new era of participatory media experience has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 762091.
The National Research National Agency of the Ministry of Science and Innovation (La Agencia Estatal de Investigación del Ministerio de Ciencia e Innovación) supports the Retos-Colaboración 2019 project New generation of music monitoring technology (NextCore), RTC2019-007248-7.
Identi@rt – R+D en preservació del valor d’actius digitals de música, art I disseny, amb distributed ledger tech – (COMRDI18-1-0011) of la Comunitat Media (COM18-1-0002) is financed by ACCIÓ (Agència per la Competitivitat de l’Empresa, Generalitat de Catalunya) as part of the collaborative projects for investigation, development and innovation of the Communities RIS3CAT.
With the support of—
ACCIÓ (Agència per la Competitivitat de l’Empresa, Generalitat de Catalunya) supports the project LoudSense – AI system for automatic audibility estimation of background music in audiovisual productions (ACE014/20/000051) as part of the call INNOTEC 2020.
With the support of—
PICAE – Publicació Intel·ligent de Continguts Audiovisuals I Editorials – (COMRDI18-1-0007) of la Comunitat Media (COM18-1-0002) is financed by ACCIÓ (Agència per la Competitivitat de l’Empresa, Generalitat de Catalunya) as part of the collaborative projects for investigation, development and innovation of the Communities RIS3CAT.
With the support of—
Ministerio de Economía y Empresa (Ministry of Economy and Enterprise) supports the Strategic Action of Economy and Digital Society – the Digital Enabling Technologies impulse 2019, project System for real-time massive audio monitoring based on artificial intelligence (DeepTrack), TSI-100903-2019-38.
Ministry of Industry, Energy and Digital Agenda (Ministerio de Energía, Turismo y Agenda Digital) supports the Strategic Action of Economy and Digital Society – Technological Impulse 2017, Project PlayIT: Big Data BI scalable engine for the process and identification of music metadata – TSI-100600-2017-20). Co-funded by Fondo Europeo de Desarrollo Regional (FEDER) – The way to build Europe.
The projects Music/Speech detection in broadcast media programs (DI46-2016) and Music identification algorithms using deep learning techniques (DI46-2020) are supported by the Industrial Doctorates plan of the Secretary of Universities and Investigation of the Department of Companies and knowledge of the Catalonia Generalitat (Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Cataluña).
Red.es (Ministry of Economic Affairs and Digital Transformation) supports the Strategic Action of Economy and Digital Society – the Digital Enabling Technologies impulse 2020, project Cloud Computing Platform for Scalable and Elastic Musical Monitoring (BigWave), 2020/0720/00097949. Co-funded by the European Union through the European Regional Development Fund (FEDER) – The way to build Europe
Red.es (Ministry of Economic Affairs and Digital Transformation) supports this R&D project in Artificial Intelligence and other digital technologies together with their integration in the value chain (C005/21-ED call) as part of the Digital Spain 2025 plan: Motor de reconciliación de entidades impulsado por IA para la industria de la música (AI-powered entity reconciliation engine for the music industry) – 2021/C005/00149157.
Ministerio para la Transformación Digital y de la Función Pública supports this R&D project in technologies associated with the Metaverse and Web 3 as part of the Spain Audiovisual Hub of Europe plan. Proof-of-Play muSIc oraCLE (PoPSICLE) – Audio Fingerprint-Based Blockchain Oracle for Decentralised Logging of Music Usage – FAV-010200-2022-0019 is financed by:
The Ministerio de Ciencia, Innovación y Universidades and the Ministerio para la Transformación Digital y de la Función Pública support this R&D project subsidised by the CDTI in new technologies applied to the Audiovisual and Videogames production as part of the State Plan for Scientific and Technical Research and Innovation 2021-2023, within the Recovery, Transformation and Resilience Plan: resCUE: Smart system for automatic usage reporting of musical works in audiovisual productions – SAV-20221147.
This project E!693 Live Insights – Turning online music streams into offline success, an AI-based platform that analyses data from digital music streaming services and social media to discover untapped revenue opportunities for performing artists, live music venues and event ticketing companies, has received funding from the Eurostars-3 joint programme with co-funding from the CDTI, supported by the Spanish Science and Innovation Ministry, and the European Union Horizon Europe research and innovation programme.
MUSIC360 – A 360 degrees perspective on the value of music has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101094872.
BMAT has been a beneficiary of the PADIH program to carry out the project. The project consists of a consultancy for specialised advice and contribution of ideas with the purpose of enhancing competitiveness. The PADIH call is financed by:
KEIA aims to establish the principles for advanced content auditing techniques in AI-generated music. It addresses the lack of transparency of current systems by investigating the use of copyrighted material for the training of models and developing strategies to obtain and identify such content in the generated material. Ultimately, KEIA seeks to ensure the ethical and sustainable use of AI in music creation. With the support of Fundació Barcelona Music Lab.
This project 6256 – Auditio: Amplify Growth: Transformative Audio Insights Platform, a novel, cost-effective, scalable, and transformative platform to provide music data and recommendations to music venues, retail and hospitality outlets, has received funding from the Eurostars-3 joint programme with co-funding from the CDTI, supported by the Spanish Ministerio de Ciencias, Innovación e Universidades, and the European Union Horizon Europe research and innovation programme.
Awards
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.
Awards
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.
Publications
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.
Publications
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.
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]
Publications
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.
Publications
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.
Awards
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.
Our Scientific Publicactions and Awards