A consolidated perspective on multimicrophone speech enhancement and source separation
Speech enhancement and separation are core problems in audio signal processing, with
commercial applications in devices as diverse as mobile phones, conference call systems …
commercial applications in devices as diverse as mobile phones, conference call systems …
Learning neural acoustic fields
Our environment is filled with rich and dynamic acoustic information. When we walk into a
cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open …
cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open …
An overview of machine learning and other data-based methods for spatial audio capture, processing, and reproduction
The domain of spatial audio comprises methods for capturing, processing, and reproducing
audio content that contains spatial information. Data-based methods are those that operate …
audio content that contains spatial information. Data-based methods are those that operate …
[HTML][HTML] Sound field reconstruction in rooms: Inpainting meets super-resolution
In this paper, a deep-learning-based method for sound field reconstruction is proposed. The
possibility to reconstruct the magnitude of the sound pressure in the frequency band 30–300 …
possibility to reconstruct the magnitude of the sound pressure in the frequency band 30–300 …
Reconstruction of the sound field in a room using compressive sensing
Capturing the impulse or frequency response functions within extended regions of a room
requires an unfeasible number of measurements. In this study, a method to reconstruct the …
requires an unfeasible number of measurements. In this study, a method to reconstruct the …
Physics-informed neural network for volumetric sound field reconstruction of speech signals
Recent developments in acoustic signal processing have seen the integration of deep
learning methodologies, alongside the continued prominence of classical wave expansion …
learning methodologies, alongside the continued prominence of classical wave expansion …
Room impulse response interpolation using a sparse spatio-temporal representation of the sound field
Room Impulse Responses (RIRs) are typically measured using a set of microphones and a
loudspeaker. When RIRs spanning a large volume are needed, many microphone …
loudspeaker. When RIRs spanning a large volume are needed, many microphone …
Generative adversarial networks with physical sound field priors
This paper presents a deep learning-based approach for the spatiotemporal reconstruction
of sound fields using generative adversarial networks. The method utilises a plane wave …
of sound fields using generative adversarial networks. The method utilises a plane wave …
Room impulse response reconstruction with physics-informed deep learning
A method is presented for estimating and reconstructing the sound field within a room using
physics-informed neural networks. By incorporating a limited set of experimental room …
physics-informed neural networks. By incorporating a limited set of experimental room …
Sound field estimation based on physics-constrained kernel interpolation adapted to environment
JGC Ribeiro, S Koyama, R Horiuchi… - … /ACM Transactions on …, 2024 - ieeexplore.ieee.org
A sound field estimation method based on kernel interpolation with an adaptive kernel
function is proposed. The kernel-interpolation-based sound field estimation methods enable …
function is proposed. The kernel-interpolation-based sound field estimation methods enable …