7 papers
Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting
Mattia Marella, Shoichi Koyama
A learning-based physics-constrained neural kernel for sound field estimation is proposed. Sound field estimation aims to estimate the spatial distribution of an acoustic field fro…
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…
Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening
Diego Di Carlo, Shoichi Koyama, Nugraha Aditya Arie +3
This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening (e.g., spatial filtering and binaural…
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…
Low-Rank Adaptation of Deep Prior Neural Networks For Room Impulse Response Reconstruction
Mirco Pezzoli, Federico Miotello, Shoichi Koyama +1
The Deep Prior framework has emerged as a powerful generative tool which can be used for reconstructing sound fields in an environment from few sparse pressure measurements. It emp…