# Deep Learning for Personalized Binaural Audio Reproduction

## TL;DR

This survey reviews recent deep learning techniques for personalized binaural audio, focusing on explicit filtering and end-to-end rendering methods, datasets, evaluation metrics, and future research directions.

## Contribution

It categorizes deep learning approaches into two paradigms, summarizes datasets and metrics, and discusses applications, limitations, and future research in personalized spatial audio.

## Key findings

- Explicit methods predict personalized HRTFs from measurements or cues.
- End-to-end methods directly map source to binaural signals with personalization.
- The survey highlights key datasets and evaluation metrics for fair comparison.

## Abstract

Personalized binaural audio reproduction is the basis of realistic spatial localization, sound externalization, and immersive listening, directly shaping user experience and listening effort. This survey reviews recent advances in deep learning for this task and organizes them by generation mechanism into two paradigms: explicit personalized filtering and end-to-end rendering. Explicit methods predict personalized head-related transfer functions (HRTFs) from sparse measurements, morphological features, or environmental cues, and then use them in the conventional rendering pipeline. End-to-end methods map source signals directly to binaural signals, aided by other inputs such as visual, textual, or parametric guidance, and they learn personalization within the model. We also summarize the field's main datasets and evaluation metrics to support fair and repeatable comparison. Finally, we conclude with a discussion of key applications enabled by these technologies, current technical limitations, and potential research directions for deep learning-based spatial audio systems.