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

Keng-Wei Chang¹, Yih-Liang Shen¹, Tai-Shih Chi¹

¹Department 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

|March 3, 2025

Summary

This summary is machine-generated.

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.

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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.