Vesa Välimäki: Machine learning and digital audio effects
When: Thursday 17th October @ 6:15 PM
Where: The Atrium (G.10), Alison House, 12 Nicholson Sq, University of Edinburgh
Title: Machine learning and digital audio effects
Speakers: Vesa Välimäki (Aalto University)
Abstract
Many new audio effects processing methods employ machine learning techniques, but this was not the case just five years ago. This talk discusses the developments that led to the paradigm shift in our research field, which followed a few years behind some closely related fields, such as speech recognition and synthesis. It has been a nice surprise that machine learning can better solve many audio processing problems than previous signal-processing methods. However, there are also counterexamples for which we have not found a perfect machine-learning-based solution. Audio time-scale modification is a problem for which ideal training data is unavailable, and the current best method is based on traditional signal processing. Generative machine learning, such as diffusion models, can provide excellent solutions to problems that seemed almost impossible earlier, such as the reconstruction of long gaps, or audio inpainting.
Vesa Välimäki is a professor of audio signal processing at Aalto University in Espoo, Finland. He received his M.Sc. (1992) and doctoral degrees (1995), both in electrical engineering, from the Helsinki University of Technology. In 1996, he was a postdoctoral researcher at the University of Westminster, London, UK. In 2008-2009, he was a visiting scholar at the Center for Computer Research in Music and Acoustics (CCRMA), Stanford University, CA, USA. Professor Välimäki’s research interests have included various topics in acoustics, audio technology, digital signal processing, machine learning, and music technology. In recent years, he has contributed, with his students and colleagues, to the use of deep learning in virtual analog modeling and audio enhancement. Professor Välimäki is a Fellow of the IEEE, AES, and Asia-Pacific Artificial Intelligence Association (AAIA). He is the Editor-in-Chief of the Journal of the Audio Engineering Society.