9 citations · 17 across the 21 of their papers we have counts for
23 papers · 1 filter
End-to-End Historical Music Restoration in Latent Space
Steven Cho, Junghyun Koo, Raphael Lafargue +3
Historical music restoration (HMR) has almost exclusively focused on constrained problems such as Super-Resolution or the restoration of solo pieces, under-exploring the general ta…
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…
Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Model
Eloi Moliner, Christoph Hold, Juan Azcarreta Ortiz +5
We address the problem of encoding room impulse responses (RIRs) into high-order Ambisonics (HOA) representations from arbitrary and potentially insufficient or incomplete micropho…
Music Restoration via Latent Operator Optimization and Diffusion Model Priors
Michal Švento, Eloi Moliner, Valtteri Kallinen +3
Music restoration seeks to recover a clean signal from an observed recording degraded by an unknown effect, distortion, or corruption. Existing systems often rely on paired trainin…
Automatic Music Mixing using a Generative Model of Effect Embeddings
Eloi Moliner, Marco A. Martínez-Ramírez, Junghyun Koo +5
Music mixing involves combining individual tracks into a cohesive mixture, a task characterized by subjectivity where multiple valid solutions exist for the same input. Existing au…
An Octave-based Multi-Resolution CQT Architecture for Diffusion-based Audio Generation
Maurício do V. M. da Costa, Eloi Moliner
This paper introduces MR-CQTdiff, a novel neural-network architecture for diffusion-based audio generation that leverages a multi-resolution Constant- Transform (CT). The pro…
Similarity-Guided Diffusion for Long-Gap Music Inpainting
Sean Turland, Eloi Moliner, Vesa Välimäki
Music inpainting aims to reconstruct missing segments of a corrupted recording. While diffusion-based generative models improve reconstruction for medium-length gaps, they often st…
Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches
Eloi Moliner, Michal Švento, Alec Wright +3
Accurately estimating nonlinear audio effects without access to paired input-output signals remains a challenging problem. This work studies unsupervised probabilistic approaches f…
Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
Michal Švento, Eloi Moliner, Lauri Juvela +2
The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult b…
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…
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…
Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data
Eloi Moliner, Sebastian Braun, Hannes Gamper
Audio domain transfer is the process of modifying audio signals to match characteristics of a different domain, while retaining the original content. This paper investigates the po…
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…
A Diffusion-Based Generative Equalizer for Music Restoration
Eloi Moliner, Maija Turunen, Filip Elvander +1
This paper presents a novel approach to audio restoration, focusing on the enhancement of low-quality music recordings, and in particular historical ones. Building upon a previous…
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…
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…
Diffusion-Based Audio Inpainting
Eloi Moliner, Vesa Välimäki
Audio inpainting aims to reconstruct missing segments in corrupted recordings. Most of existing methods produce plausible reconstructions when the gap lengths are short, but strugg…
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…
Realistic Gramophone Noise Synthesis using a Diffusion Model
Eloi Moliner, Vesa Välimäki
This paper introduces a novel data-driven strategy for synthesizing gramophone noise audio textures. A diffusion probabilistic model is applied to generate highly realistic quasipe…
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…