Thomas Sherson — accepted AI-conference papers

2023

End-to-End Amp Modeling: from Data to Controllable Guitar Amplifier Models

Lauri Juvela, Eero-Pekka Damskägg, Aleksi Peussa, Jaakko Mäkinen, Thomas Sherson, Stylianos I. Mimilakis +2

ICASSP 2023accepted

This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers’ sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeline, w…

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2018

Quantisation Effects in Distributed Optimisation

Joseph A. G. Jonkman, Thomas Sherson, Richard Heusdens

ICASSP 2018accepted

In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexa…

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2017

Quantisation effects in PDMM: A first study for synchronous distributed averaging

Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens

ICASSP 2017accepted

Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algo…

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2016

A distributed algorithm for robust LCMV beamforming

Thomas Sherson, W. Bastiaan Kleijn, Richard Heusdens

ICASSP 2016accepted

In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks,…

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