no code implementations • 22 Dec 2023 • Eloi Moliner, Leonardo Fierro, Alec Wright, Matti Hämäläinen, Vesa Välimäki
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 processing of the latter component.
no code implementations • 26 May 2023 • Otto Mikkonen, Alec Wright, Eloi Moliner, Vesa Välimäki
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 physics of the magnetic recording process.
no code implementations • 30 Nov 2022 • Leonardo Fierro, Alec Wright, Vesa Välimäki, Matti Hämäläinen
A deep neural network solution for time-scale modification (TSM) focused on large stretching factors is proposed, targeting environmental sounds.
no code implementations • 2 Nov 2022 • 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.
1 code implementation • 4 May 2022 • Jan Wilczek, Alec Wright, Vesa Välimäki, Emanuël Habets
Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems.
no code implementations • 20 Nov 2019 • 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.