Head-Related Transfer Function Individualization Using Anthropometric Features and Spatially Independent Latent Representation

TL;DR

This paper introduces a deep learning approach that uses anthropometric features and a spatially independent latent space to personalize head-related transfer functions, improving accuracy and dataset integration.

Contribution

It presents a novel HRTF individualization method leveraging autoencoders and latent representations, enabling effective dataset combination and reduced parameter estimation.

Findings

Achieves high estimation accuracy compared to existing DNN methods.

Enables integration of multiple HRTF datasets with different source positions.

Reduces the complexity of anthropometric parameter estimation.

Abstract

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 subjects included in many HRTF datasets is limited, and the number of those that include anthropometric parameters is even smaller. Therefore, HRTF individualization based on deep neural networks (DNNs) is a challenging task. We propose a HRTF individualization method using the latent representation of HRTF magnitude obtained through an autoencoder conditioned on sound source positions, which makes it possible to combine multiple HRTF datasets with different measured source positions, and makes the network training tractable by reducing the number of parameters to be estimated from anthropometric parameters. Experimental evaluation shows that high estimation accuracy is achieved by the proposed method, compared to current DNN-based methods.