Spatial grouping as a method to improve personalized head-related transfer function prediction

Keng-Wei Chang ¹, Yih-Liang Shen ¹, Tai-Shih Chi ¹

⁰Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Taiwan 300093alex0976296586@gmail.com, yihliang.ee06@nycu.edu.tw, tschi@nycu.edu.tw.

Jasa Express Letters +

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March 3, 2025

View abstract on PubMed

Summary

We propose a novel method for estimating head-related transfer functions (HRTFs) by spatially grouping HRTF data. This approach balances computational efficiency and performance, outperforming existing models.

Area Of Science

Acoustics and Audio Engineering
Machine Learning and Artificial Intelligence

Background

Head-related transfer functions (HRTFs) are crucial for spatial audio perception.
Accurate HRTF estimation is computationally intensive, especially with neural network models.
Existing angle-specific models offer high performance but require significant computational resources.

Purpose Of The Study

To develop a computationally efficient method for HRTF estimation.
To improve the performance of neural network-based HRTF prediction models.
To balance performance and computational cost in HRTF estimation.

Main Methods

Proposing a novel method involving spatial grouping of HRTF data into subspaces.
Reducing variance within each subspace through targeted data grouping.
Training individual HRTF predicting neural networks for each subspace.

Main Results

The proposed method demonstrates superior performance compared to global models.
The spatial grouping approach outperforms traditional angle-specific models.
Different grouping strategies show effectiveness for ipsilateral and contralateral sides.

Conclusions

Spatially grouping HRTF data offers an effective strategy for improving prediction accuracy.
The proposed method provides a balanced solution for HRTF estimation, optimizing performance and computational load.
This technique advances the field of personalized spatial audio and virtual acoustics.