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| Centre for Digital Music | |
|---|---|
| Name | Centre for Digital Music |
| Established | 2001 |
| Type | Research centre |
| Parent | Queen Mary University of London |
| City | London |
| Country | United Kingdom |
Centre for Digital Music is an interdisciplinary research centre at Queen Mary University of London focused on computational audio, music information retrieval, and sound technology. The centre integrates methods from computer science, electrical engineering, psychology, musicology, and signal processing to address problems in audio analysis, synthesis, and human–computer interaction. Researchers collaborate with institutions such as IRCAM, BBC, Microsoft Research, Spotify, and Apple Inc. on projects spanning algorithmic composition, audio forensics, and perceptual modelling.
Founded in 2001 within Queen Mary University of London, the centre grew from collaborations among researchers with backgrounds at University of Cambridge, University of Oxford, University College London, Princeton University, and McGill University. Early funding arrived from bodies including the Engineering and Physical Sciences Research Council, the Arts and Humanities Research Council, and the European Research Council, supporting projects that connected to initiatives at Music Information Retrieval Evaluation eXchange, ISCA, and ICASSP. Over time the centre established ties with industry partners such as Sony, Nokia, Google, Amazon (company), and Baidu while contributing to conferences like International Society for Music Information Retrieval Conference, AES Convention, and NIME Conference.
The centre's work spans music information retrieval, audio signal processing, machine learning, computational creativity, and psychoacoustics. Projects address problems in automatic transcription, source separation, beat tracking, tempo estimation, timbre analysis, and audio classification using techniques from deep learning, probabilistic modelling, Bayesian statistics, neural networks, and sparse representations. Interdisciplinary studies relate to music perception, cognitive neuroscience, human–computer interaction, and interactive music systems with reference to datasets and challenges linked to Million Song Dataset, GTZAN dataset, Mir-1k, and MedleyDB.
Facilities include recording studios comparable to those at Abbey Road Studios for acoustic capture, anechoic measurement spaces akin to labs at Fraunhofer Society, and computing clusters similar to resources at European Grid Infrastructure. Technical resources encompass high‑performance GPUs from vendors like NVIDIA, audio analysers such as those used by Dolby Laboratories, and software toolkits in the tradition of MATLAB, Python (programming language), TensorFlow, and PyTorch. The centre maintains curated corpora and benchmarks used by communities around ISWC, ICML, NeurIPS, and ICASSP.
Academic programmes include postgraduate supervision for PhD candidates, taught modules for Master of Science students, and short courses modeled after workshops at IEEE, Royal Academy of Music, and Guildhall School of Music and Drama. Training emphasises skills relevant to employers like BBC Research & Development, Shazam Entertainment, Sonos, and Ableton AG by combining coursework in signal processing, machine learning, software engineering, and music theory. Students participate in exchange and internship schemes with institutions such as IRCAM, McGill University Schulich School of Music, Royal College of Music, and companies including Facebook AI Research.
Partnerships include research collaborations with Spotify Technology S.A., Apple Inc., Google Research, Microsoft Research, Amazon Music, and public organisations such as BBC. The centre has engaged in European consortia alongside ETH Zurich, University of Amsterdam, Technical University of Berlin, University of Music and Performing Arts Vienna, and Tilburg University. Collaborative outputs have appeared in venues including Proceedings of the National Academy of Sciences, Nature Communications, IEEE Transactions on Audio, Speech, and Language Processing, and Journal of the Acoustical Society of America.
Key contributions include advancements in audio source separation algorithms, improvements to automatic music transcription systems, and development of perceptual models for audio quality assessment. The centre produced influential open datasets and toolkits referenced by projects at Spotify, Shazam, Pandora (streaming service), and research groups at MIT Media Lab, Stanford University, Carnegie Mellon University, and Rensselaer Polytechnic Institute. Collaborative projects addressed applications in audio forensics, hearing aid algorithm design with partners like Cochlear Limited, and immersive audio formats related to Dolby Laboratories and THX Ltd..
Researchers have received awards from bodies such as the Royal Academy of Engineering, the Royal Society, the European Research Council, the IEEE Signal Processing Society, and the Acoustical Society of America. Individual faculty and alumni have been recognised by prizes including the ISG Best Paper Award, the IEEE Signal Processing Society Best Paper Award, the ACM SIGMM Technical Achievement Award, and fellowships from EPSRC and Marie Skłodowska-Curie Actions. The centre's outputs have been cited in award-winning projects at events like SXSW, BAFTA, and Ivor Novello Awards.
Category:Research institutes in London Category:Music technology