This paper presents a comprehensive review of the design of experiments used in the
surrogate models. In particular, this study demonstrates the necessity of the design of …

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 …

Recently deep learning and machine learning approaches have been widely employed for
various applications in acoustics. Nonetheless, in the area of sound field processing and …

Deep learning (DL) relies heavily on data, and the quality of data influences its performance
significantly. However, obtaining high-quality, well-annotated datasets can be challenging or …

Recent developments in acoustic signal processing have seen the integration of deep
learning methodologies, alongside the continued prominence of classical wave expansion …

Acoustic simulations often face significant uncertainties due to limited knowledge of acoustic
boundary conditions. While measuring the boundary admittance in situ is challenging in …

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 …

Accurate estimation of the sound field around a rigid sphere necessitates adequate
sampling on the sphere, which may not always be possible. To overcome this challenge, this …

In this paper, we propose a data-driven approach for the reconstruction of unknown room
impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR …

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). In this paper, we propose a …