4 papers
SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements
Ege Erdem, Shoichi Koyama, Tomohiko Nakamura +2
Reconstructing a 3D sound field from sparse microphone measurements is a fundamental yet ill-posed problem, which we address through Acoustic Transfer Function (ATF) magnitude esti…
Phase-Retrieval-Based Physics-Informed Neural Networks For Acoustic Magnitude Field Reconstruction
Karl Schrader, Shoichi Koyama, Tomohiko Nakamura +1
We propose a method for estimating the magnitude distribution of an acoustic field from spatially sparse magnitude measurements. Such a method is useful when phase measurements are…
Head-Related Transfer Function Individualization Using Anthropometric Features and Spatially Independent Latent Representation
Ryan Niu, Shoichi Koyama, Tomohiko Nakamura
A method for head-related transfer function (HRTF) individualization from the subject's anthropometric parameters is proposed. Due to the high cost of measurement, the number of su…
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…