3 papers
eess.AS2026
SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements
Ege Erdem, Shoichi Koyama, Tomohiko Nakamura +3
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…
cs.SD2026
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…
cs.SD2025
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…