Work

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

Year: 2025

Type: article

Abstract: The head-related transfer function (HRTF) characterizes the frequency response of the sound traveling path between a specific location and the ear. When it comes to estimating HRTFs by neural network models, angle-specific models greatly outperform global models but demand high computational resources. To balance the computational resource and performance, we propose a method by grouping HRTF data spatially to reduce variance within each subspace. HRTF predicting neural network is then trained for each subspace. Results show the proposed method performs better than global models and angle-specific models by using different grouping strategies at the ipsilateral and contralateral sides.

Source: JASA Express Letters

DOI: 10.1121/10.0036032

Authors: Keng-Wei Chang, Yih-Liang Shen, Tai-Shih Chi

Institution: National Yang Ming Chiao Tung University

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Ministry of Science and Technology, Taiwan

OpenAlex ID: W4408122671