5 citations · 19 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Solving Audio Inverse Problems with a Diffusion Model
Eloi Moliner, Jaakko Lehtinen, Vesa Välimäki
This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic settin…
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…
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Eloi Moliner, Vesa Välimäki
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of exte…
A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings
Eloi Moliner, Vesa Välimäki
Enhancing the sound quality of historical music recordings is a long-standing problem. This paper presents a novel denoising method based on a fully-convolutional deep neural netwo…