5 citations · 19 across the 23 of their papers we have counts for
29 papers
Perception-Inspired Bayesian Causal Fusion for Audiovisual Source Localization
Kyung Yun Lee, Sungnyun Kim, Sebastian J. Schlecht +2
Multimodal fusion promises more accurate perception but only when the modalities share a common cause. When they do not, the second modality carries no information about the target…
Music Source Separation via Stem Discovery
V. Valtteri Kallinen, Eloi Moliner, Lauri Juvela +1
Music source separation (MSS) methods aim to extract stems from music mixtures, which is important, for example, in karaoke, music remixing, and pedagogical applications. While ear…
Estimation of Room Impulse Responses from Handclaps
Shih-Yu Lai, Kyung Yun Lee, Nils Meyer-Kahlen +3
Handclaps provide an equipment-free excitation for room acoustics, but their unknown and variable source waveform makes room impulse response (RIR) estimation challenging. In this…
Exact Factorisation and Fast Computation of Invertible Constant-Q Transforms
Facundo Franchino, Eloi Moliner, Vesa Välimäki
The constant-Q transform (CQT) represents audio on a logarithmic frequency axis. Its nonstationary Gabor formulation is exactly invertible, but the unequal numbers of time coeffici…
State-Space-Based FIR Filtering on a Quantum Computer
Roope Salmi, Davide Rocchesso, Vesa Välimäki
Many signal processing tasks require intensive computations. Quantum computing promises to accelerate certain tasks, but algorithms must be designed around the limitations of quant…
Four Decades of Digital Waveguides
Pablo Tablas de Paula, Julius O. Smith, Vesa Välimäki +1
Digital waveguide physical modeling offers efficient simulation of acoustic wave propagation as compared to general finite-difference schemes commonly used in computational physics…
Resampling Filter Design for Multirate Neural Audio Effect Processing
Alistair Carson, Vesa Välimäki, Alec Wright +1
Neural networks have become ubiquitous in audio effects modelling, especially for guitar amplifiers and distortion pedals. One limitation of such models is that the sample rate of…
HRTF Estimation using a Score-based Prior
Etienne Thuillier, Jean-Marie Lemercier, Eloi Moliner +2
We present a head-related transfer function (HRTF) estimation method which relies on a data-driven prior given by a score-based diffusion model. The HRTF is estimated in reverberan…
Similarity Metrics For Late Reverberation
Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht +1
Automatic tuning of reverberation algorithms relies on the optimization of a cost function. While general audio similarity metrics are useful, they are not optimized for the specif…
Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models
Jean-Marie Lemercier, Eloi Moliner, Simon Welker +2
This paper presents an unsupervised method for single-channel blind dereverberation and room impulse response (RIR) estimation, called BUDDy. The algorithm is rooted in Bayesian po…
Fade-in Reverberation in Multi-room Environments Using the Common-Slope Model
Kyung Yun Lee, Nils Meyer-Kahlen, Georg Götz +3
In multi-room environments, modelling the sound propagation is complex due to the coupling of rooms and diverse source-receiver positions. A common scenario is when the source and…
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…
BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion Models
Eloi Moliner, Jean-Marie Lemercier, Simon Welker +2
In this paper, we present an unsupervised single-channel method for joint blind dereverberation and room impulse response estimation, based on posterior sampling with diffusion mod…
Non-Exponential Reverberation Modeling Using Dark Velvet Noise
Jon Fagerström, Sebastian J. Schlecht, Vesa Välimäki
Previous research on late-reverberation modeling has mainly focused on exponentially decaying room impulse responses, whereas methods for accurately modeling non-exponential reverb…
Optimizing tiny colorless feedback delay networks
Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht +1
A common bane of artificial reverberation algorithms is spectral coloration in the synthesized sound, typically manifesting as metallic ringing, leading to a degradation in the per…
Diffusion Models for Audio Restoration
Jean-Marie Lemercier, Julius Richter, Simon Welker +3
With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for…
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…
HRTF Interpolation using a Spherical Neural Process Meta-Learner
Etienne Thuillier, Craig Jin, Vesa Välimäki
Several individualization methods have recently been proposed to estimate a subject's Head-Related Transfer Function (HRTF) using convenient input modalities such as anthropometric…
Blind Audio Bandwidth Extension: A Diffusion-Based Zero-Shot Approach
Eloi Moliner, Filip Elvander, Vesa Välimäki
Audio bandwidth extension involves the realistic reconstruction of high-frequency spectra from bandlimited observations. In cases where the lowpass degradation is unknown, such as…
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…
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…