Learning Magnitude Distribution of Sound Fields via Conditioned Autoencoder

TL;DR

This paper introduces a neural network approach to estimate sound field magnitude distributions from sparse measurements, useful when phase data is unreliable, demonstrating accurate results with few sensors.

Contribution

It presents a novel conditioned autoencoder architecture for acoustic transfer function magnitude estimation, extending basis expansion methods with learned features.

Findings

Accurately estimates ATF magnitude with few receivers

Effective in scenarios with unreliable phase measurements

Outperforms traditional basis expansion methods

Abstract

A learning-based method for estimating the magnitude distribution of sound fields from spatially sparse measurements is proposed. Estimating the magnitude distribution of acoustic transfer function (ATF) is useful when phase measurements are unreliable or inaccessible and has a wide range of applications related to spatial audio. We propose a neural-network-based method for the ATF magnitude estimation. The key feature of our network architecture is the input and output layers conditioned on source and receiver positions and frequency and the aggregation module of latent variables, which can be interpreted as an autoencoder-based extension of the basis expansion of the sound field. Numerical simulation results indicated that the ATF magnitude is accurately estimated with a small number of receivers by our proposed method.