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 +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…
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…
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…
Learning Magnitude Distribution of Sound Fields via Conditioned Autoencoder
Shoichi Koyama, Kenji Ishizuka
A learning-based method for estimating the magnitude distribution of sound fields from spatially sparse measurements is proposed. Estimating the magnitude distribution of acoustic…