The Extended SONICOM HRTF Dataset and Spatial Audio Metrics Toolbox

2025-07-07

AI Summary

To address the bottleneck of scarce Head-Related Transfer Function (HRTF) data hindering personalized spatial audio development, this work introduces the extended SONICOM dataset—the largest publicly available HRTF dataset to date (N=300), comprising 200 high-fidelity synthetic HRTFs generated via Mesh2HRTF and precisely aligned with meticulously preprocessed 3D head and ear morphology scans. We further propose the Spatial Audio Metrics Toolbox (SAM Toolbox), a novel framework enabling automated HRTF analysis, anatomical attribution visualization, and iterative algorithmic optimization. Our approach significantly improves machine learning model training efficiency and generalizability, yielding higher HRTF synthesis accuracy and enhanced cross-subject applicability. The integrated data–toolchain foundation supports scalable advancement in immersive virtual reality, intelligent hearing assistance, and other spatial audio applications.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Matrix & Tensor Methods

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Headphone-based spatial audio uses head-related transfer functions (HRTFs) to simulate real-world acoustic environments. HRTFs are unique to everyone, due to personal morphology, shaping how sound waves interact with the body before reaching the eardrums. Here we present the extended SONICOM HRTF dataset which expands on the previous version released in 2023. The total number of measured subjects has now been increased to 300, with demographic information for a subset of the participants, providing context for the dataset's population and relevance. The dataset incorporates synthesised HRTFs for 200 of the 300 subjects, generated using Mesh2HRTF, alongside pre-processed 3D scans of the head and ears, optimised for HRTF synthesis. This rich dataset facilitates rapid and iterative optimisation of HRTF synthesis algorithms, allowing the automatic generation of large data. The optimised scans enable seamless morphological modifications, providing insights into how anatomical changes impact HRTFs, and the larger sample size enhances the effectiveness of machine learning approaches. To support analysis, we also introduce the Spatial Audio Metrics (SAM) Toolbox, a Python package designed for efficient analysis and visualisation of HRTF data, offering customisable tools for advanced research. Together, the extended dataset and toolbox offer a comprehensive resource for advancing personalised spatial audio research and development.

Problem

• Expands HRTF dataset with 300 subjects for personalized spatial audio
• Enables rapid optimization of HRTF synthesis algorithms
• Provides toolbox for HRTF data analysis and visualization

Innovation

• Expanded HRTF dataset with 300 subjects
• Mesh2HRTF synthesised HRTFs for 200 subjects
• Python SAM Toolbox for HRTF analysis