7 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
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…
Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations
Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari
An interpolation method for region-to-region acoustic transfer functions (ATFs) based on kernel ridge regression with an adaptive kernel is proposed. Most current ATF interpolation…
Region-to-region kernel interpolation of acoustic transfer function with directional weighting
Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari
A method of interpolating the acoustic transfer function (ATF) between regions that takes into account both the physical properties of the ATF and the directionality of region conf…
Kernel Learning For Sound Field Estimation With L1 and L2 Regularizations
Ryosuke Horiuchi, Shoichi Koyama, Juliano G. C. Ribeiro +2
A method to estimate an acoustic field from discrete microphone measurements is proposed. A kernel-interpolation-based method using the kernel function formulated for sound field i…