3 citations · 5 across the 9 of their papers we have counts for
9 papers
Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches
Eloi Moliner, Michal Švento, Alec Wright +3
Accurately estimating nonlinear audio effects without access to paired input-output signals remains a challenging problem. This work studies unsupervised probabilistic approaches f…
Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
Michal Švento, Eloi Moliner, Lauri Juvela +2
The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult b…
Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing
Alistair Carson, Alec Wright, Jatin Chowdhury +2
In recent years, machine learning approaches to modelling guitar amplifiers and effects pedals have been widely investigated and have become standard practice in some consumer prod…
Noise Morphing for Audio Time Stretching
Eloi Moliner, Leonardo Fierro, Alec Wright +2
This letter introduces an innovative method to enhance the quality of audio time stretching by precisely decomposing a sound into sines, transients, and noise and by improving the…
Neural modeling of magnetic tape recorders
Otto Mikkonen, Alec Wright, Eloi Moliner +1
The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the…
Extreme Audio Time Stretching Using Neural Synthesis
Leonardo Fierro, Alec Wright, Vesa Välimäki +1
A deep neural network solution for time-scale modification (TSM) focused on large stretching factors is proposed, targeting environmental sounds. Traditional TSM artifacts such as…
Adversarial Guitar Amplifier Modelling With Unpaired Data
Alec Wright, Vesa Välimäki, Lauri Juvela
We propose an audio effects processing framework that learns to emulate a target electric guitar tone from a recording. We train a deep neural network using an adversarial approach…
Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations
Jan Wilczek, Alec Wright, Vesa Välimäki +1
Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog…
Perceptual Loss Function for Neural Modelling of Audio Systems
Alec Wright, Vesa Välimäki
This work investigates alternate pre-emphasis filters used as part of the loss function during neural network training for nonlinear audio processing. In our previous work, the err…