37 citations · 88 across the 28 of their papers we have counts for
7 papers · 1 filter
Fast Diffusion-GAN Model for Generating Symbolic Music with Emotions
Jincheng Zhang, György Fazekas, Charalampos Saitis
Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. However, little progress has been made o…
Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models
Jincheng Zhang, György Fazekas, Charalampos Saitis
Emerging Denoising Diffusion Probabilistic Models (DDPM) have become increasingly utilised because of promising results they have achieved in diverse generative tasks with continuo…
Differentiable Modelling of Percussive Audio with Transient and Spectral Synthesis
Jordie Shier, Franco Caspe, Andrew Robertson +3
Differentiable digital signal processing (DDSP) techniques, including methods for audio synthesis, have gained attention in recent years and lend themselves to interpretability in…
A Review of Differentiable Digital Signal Processing for Music & Speech Synthesis
Ben Hayes, Jordie Shier, György Fazekas +2
The term "differentiable digital signal processing" describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilita…
Interactive Neural Resonators
Rodrigo Diaz, Charalampos Saitis, Mark Sandler
In this work, we propose a method for the controllable synthesis of real-time contact sounds using neural resonators. Previous works have used physically inspired statistical metho…
The language of sounds unheard: Exploring musical timbre semantics of large language models
Kai Siedenburg, Charalampos Saitis
Semantic dimensions of sound have been playing a central role in understanding the nature of auditory sensory experience as well as the broader relation between perception, languag…
The Responsibility Problem in Neural Networks with Unordered Targets
Ben Hayes, Charalampos Saitis, György Fazekas
We discuss the discontinuities that arise when mapping unordered objects to neural network outputs of fixed permutation, referred to as the responsibility problem. Prior work has p…