4 citations · 11 across the 11 of their papers we have counts for
15 papers
Time-domain sound field estimation using kernel ridge regression
Jesper Brunnström, Martin Bo Møller, Jan Østergaard +3
Sound field estimation methods based on kernel ridge regression have proven effective, allowing for strict enforcement of physical properties, in addition to the inclusion of prior…
Physics-Informed Machine Learning For Sound Field Estimation
Shoichi Koyama, Juliano G. C. Ribeiro, Tomohiko Nakamura +2
The area of study concerning the estimation of spatial sound, i.e., the distribution of a physical quantity of sound such as acoustic pressure, is called sound field estimation, wh…
Sound Field Estimation Using Deep Kernel Learning Regularized by the Wave Equation
David Sundström, Shoichi Koyama, Andreas Jakobsson
In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound fiel…
Localizing Acoustic Energy in Sound Field Synthesis by Directionally Weighted Exterior Radiation Suppression
Yoshihide Tomita, Shoichi Koyama, Hiroshi Saruwatari
A method for synthesizing the desired sound field while suppressing the exterior radiation power with directional weighting is proposed. The exterior radiation from the loudspeaker…
TELAMON: Effelsberg Monitoring of AGN Jets with Very-High-Energy Astroparticle Emissions -- Polarization properties
J. Heßdörfer, M. Kadler, P. Benke +33
We present recent results of the TELAMON program, which is using the Effelsberg 100-m telescope to monitor the radio spectra of active galactic nuclei (AGN) under scrutiny in astro…
Kernel Interpolation of Incident Sound Field in Region Including Scattering Objects
Shoichi Koyama, Masaki Nakada, Juliano G. C. Ribeiro +1
A method for estimating the incident sound field inside a region containing scattering objects is proposed. The sound field estimation method has various applications, such as spat…