Mark Plumbley

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Mark plumbley

Professor Mark Plumbley​

University of Surrey, UK
Professor of Signal Processing
Surrey, UK

Mark Plumbley is Professor of Signal Processing at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey, in Guildford, UK. He is an expert on analysis and processing of audio and music, using a wide range of signal processing and machine learning methods. He led the first international data challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2013), and hosted the DCASE 2018 Workshop in Woking, Surrey. He currently leads the EPSRC-funded project "Making Sense of Sounds" on automatic recognition of everyday sounds, and he is a co-editor of the recent book on "Computational Analysis of Sound Scenes and Events" (Springer, 2018).

Research interests​

My research concerns AI for Sound: using machine learning and signal processing for analysis and recognition of sounds. My focus is on detection, classification and separation of acoustic scenes and events, particularly real-world sounds, using methods such as deep learning, sparse representations and probabilistic models.

I have published over 400 papers in journals, conferences and books, including over 70 journal papers and the recent Springer co-edited book on Computational Analysis of Sound Scenes and Events.

Much of my research is funded by grants from EPSRC and EU, Innovate UK and other sources. I currently hold an EPSRC Fellowship on "AI for Sound", and recently led EPSRC projects Making Sense of Sounds and Musical Audio Repurposing using Source Separation, and two EU research training networks, SpaRTaN and MacSeNet. My total grant funding is around £54M, including £20M as Principal Investigator, Coordinator or Lead Applicant.

I was co-Chair of the DCASE 2018 Workshop on Detection and Classification of Acoustic Scenes and Events, co-Chair of the 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018) and co-Chair of the Signal Processing with Adaptive Sparse Structured Representations (SPARS 2017) workshop.