10 citations · 20 across the 10 of their papers we have counts for
12 papers
Synth-JEPA: Joint Embedding Prediction for Renderer-Free Synthesizer Parameter Search
Ben Hayes, Haokun Tian, Stefan Lattner
Sound matching can be formulated as optimizing synthesizer parameters against an audio-domain objective. However, objectives derived from generic audio representations are often di…
Off-manifold robustness in synthesizer inversion with joint distribution flow matching
Ben Hayes
Recent work on synthesizer inversion shows that generative models outperform deterministic approaches by explicitly modeling the ambiguity in mapping audio to parameters. Training…
LiveBand: Live Accompaniment Generation in the Audio Domain
Marco Pasini, Javier Nistal, Ben Hayes +3
We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints. Our method trains a causal tran…
PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective
Alain Riou, Bernardo Torres, Ben Hayes +4
In this paper, we introduce PESTO, a self-supervised learning approach for single-pitch estimation using a Siamese architecture. Our model processes individual frames of a Variable…
Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching
Ben Hayes, Charalampos Saitis, György Fazekas
Many audio synthesizers can produce the same signal given different parameter configurations, meaning the inversion from sound to parameters is an inherently ill-posed problem. We…
DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions
Chin-Yun Yu, Marco A. Martínez-Ramírez, Junghyun Koo +4
This study introduces a novel and interpretable model, DiffVox, for matching vocal effects in music production. DiffVox, short for ``Differentiable Vocal Fx", integrates parametric…
AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning
Giovanni Bindi, Nils Demerlé, Rodrigo Diaz +18
Artificial Intelligence (AI) technologies such as deep learning are evolving very quickly bringing many changes to our everyday lives. To explore the future impact and potential of…
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…
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…
Sinusoidal Frequency Estimation by Gradient Descent
Ben Hayes, Charalampos Saitis, György Fazekas
Sinusoidal parameter estimation is a fundamental task in applications from spectral analysis to time-series forecasting. Estimating the sinusoidal frequency parameter by gradient d…
Rigid-Body Sound Synthesis with Differentiable Modal Resonators
Rodrigo Diaz, Ben Hayes, Charalampos Saitis +2
Physical models of rigid bodies are used for sound synthesis in applications from virtual environments to music production. Traditional methods such as modal synthesis often rely o…
Neural Waveshaping Synthesis
Ben Hayes, Charalampos Saitis, György Fazekas
We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal approach to neural audio synthesis which operates directly in the waveform domain, with an accompa…