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A Comparison of Virtual Analog Modelling Techniques (Part 2) - Christopher Johann Clarke & Jatin Chowdhury - ADC23

This talk will explore the spectrum of virtual analog modelling techniques including traditional methods (modified nodal analysis, wave digital filters), single-architecture neural network models, and grey-box methods that incorporate both physical modelling and machine learning techniques. Several models of the gain stage from the Boss DS-1 guitar pedal will be provided as a motivating example. The talk will discuss how these methods can generalize over a wide range of circuits, as well as the specific problems that users of each modelling technique can expect to see for different types of circuits.

Link to Slides:
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Christopher Johann Clarke

Christopher Clarke is a PhD candidate studying at SUTD (Multiphysics, A.I/Machine Learning) with a background in music specialising in generative algorithms (MMus) and psychophysics in music (BA, recipient of Phillip Holt Award).
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Jatin Chowdhury

Jatin is an audio signal processing engineer from Denver, Colorado, USA. For the past several years he has worked as a developer of audio effects and other music technology software. Jatin is a graduate from the Center for Computer Research in Music and Acoustics (CCRMA) at Stanford University, where he studied audio signal processing.
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Streamed & Edited by Digital Medium Ltd: https://online.digital-medium.co.uk
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Organized and produced by JUCE: https://juce.com/
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Special thanks to the ADC23 Team:

Sophie Carus
Derek Heimlich
Andrew Kirk
Bobby Lombardi
Tom Poole
Ralph Richbourg
Jim Roper
Jonathan Roper
Prashant Mishra

#adc #audiodev #dsp #audio