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Found 19 papers, 7 papers with code
Non-Exponential Reverberation Modeling Using Dark Velvet Noise
This paper extends the previously proposed basic dark-velvet-noise reverberation algorithm and proposes a parametrization scheme for modeling late reverberation with arbitrary temporal energy decay.
A Diffusion-Based Generative Equalizer for Music Restoration
This paper presents a novel approach to audio restoration, focusing on the enhancement of low-quality music recordings, and in particular historical ones.
Feedback Delay Network Optimization
A common bane of artificial reverberation algorithms is spectral coloration, typically manifesting as metallic ringing, leading to a degradation in the perceived sound quality.
Diffusion Models for Audio Restoration
Here, we aim to show that diffusion models can combine the best of both worlds and offer the opportunity to design audio restoration algorithms with a good degree of interpretability and a remarkable performance in terms of sound quality.
Noise Morphing for Audio Time Stretching
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.
HRTF Interpolation using a Spherical Neural Process Meta-Learner
Several individualization methods have recently been proposed to estimate a subject's Head-Related Transfer Function (HRTF) using convenient input modalities such as anthropometric measurements or pinnae photographs.
Blind Audio Bandwidth Extension: A Diffusion-Based Zero-Shot Approach
In cases where the lowpass degradation is unknown, such as in restoring historical audio recordings, this becomes a blind problem.
Neural modeling of magnetic tape recorders
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.
Diffusion-Based Audio Inpainting
The proposed method uses an unconditionally trained generative model, which can be conditioned in a zero-shot fashion for audio inpainting, and is able to regenerate gaps of any size.
Extreme Audio Time Stretching Using Neural Synthesis
A deep neural network solution for time-scale modification (TSM) focused on large stretching factors is proposed, targeting environmental sounds.
Adversarial Guitar Amplifier Modelling With Unpaired Data
We propose an audio effects processing framework that learns to emulate a target electric guitar tone from a recording.
Solving Audio Inverse Problems with a Diffusion Model
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 setting.
Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise
The current solutions for this three-way separation detect either horizontal and vertical structures or anisotropy and orientations in the spectrogram to identify the properties of each spectral bin and classify it as sinusoidal, transient, or noise.
Realistic Gramophone Noise Synthesis using a Diffusion Model
A diffusion probabilistic model is applied to generate highly realistic quasiperiodic noises.
Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations
Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems.
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals.
A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings
Enhancing the sound quality of historical music recordings is a long-standing problem.
A Method for Capturing and Reproducing Directional Reverberation in Six Degrees of Freedom
A common approach is to capture an impulse response in a hall and auralize it by convolving an input signal with the measured reverberant response.
Perceptual Loss Function for Neural Modelling of Audio Systems
This work investigates alternate pre-emphasis filters used as part of the loss function during neural network training for nonlinear audio processing.
