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Found 7 papers, 1 papers with code
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-based approaches, particularly in sound field reconstruction.
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario.
Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones
Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several impor- tant real-world applications.
Reconstruction of Sound Field through Diffusion Models
Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR).
Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses
Recently deep learning and machine learning approaches have been widely employed for various applications in acoustics.
Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations
Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays.
Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network
Near-field Acoustic Holography (NAH) is a well-known problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements.
