AI for Sound - Mark Plumbley (Surrey)

I’m very excited to announce that Prof. Mark Plumbley from the University of Surrey (UK) will be visiting CCRMA on Friday and will lead the discussion of AI for sound. Mark has published ground breaking papers on audio scene classification, audio auto encoders and inpainting, spare representations, musical beat tracking and transcription. All good applications for signal processing and artificial intelligence.

Who: Prof. Mark Plumbley (Surrey)
What: AI for Sound
When: Fri, 03/17/2023 - 10:30am - 12:00pm
Where: CCRMA Seminar Room
Why: AI is good for sound!

Come to CCRMA to talk about many applications for artificial intelligence and sound. Mark has worked on many of them, and it will be fun to talk about future directions. What would you like to do with sound if we could be more intelligent?

- Malcolm



Title: AI for Sound
Prof Mark Plumbley
University of Surrey

Abstract

Imagine you are standing on a street corner in a city. Close your eyes: what do you hear? Perhaps some cars and busses driving on the road, footsteps of people on the pavement, beeps from a pedestrian crossing, and the hubbub of talking shoppers. You can do the same in a kitchen as someone is making breakfast, or as you are travelling in a vehicle. Now, following the success of AI and machine learning technologies for speech and image recognition, we are beginning to build computer systems to automatically recognize real-world sound scenes and events. In this talk, we will explore some of the work going on in this rapidly expanding research area, and discuss some of the potential applications emerging for sound recognition, from assisted living to environmental noise and sound archives. We will also outline how we are adopting participatory methods to help us realise the potential benefit of sound sensing to society and the economy.

Bio

Prof. 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, 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), and is a co-editor of the book "Computational Analysis of Sound Scenes and Events" (Springer, 2018). He currently holds a 5-year EPSRC Fellowship "AI for Sound" on automatic recognition of everyday sounds. He is a Member of the IEEE Signal Processing Society Technical Committee on Audio and Acoustic Signal Processing, and a Fellow of the IET and IEEE.