Open Access

ARTICLE

Representation of HRTF Based on Common-Pole/Zero Modeling and Principal Component Analysis

Wei Chen^1,*, Xiaogang Wei^2,*, Hongxu Zhang², Wenpeng He²

1 School of Software Henan Polytechnic University, Jiaozuo, 454000, China
2 College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China

* Corresponding Authors: Wei Chen. Email: ; Xiaogang Wei. Email:

Journal on Artificial Intelligence 2024, 6, 225-240. https://doi.org/10.32604/jai.2024.052366

Received 31 March 2024; Accepted 16 July 2024; Issue published 16 August 2024

Abstract

The Head-Related Transfer Function (HRTF) describes the effects of sound reflection and scattering caused by the environment and the human body when sound signals are transmitted from a source to the human ear. It contains a significant amount of auditory cue information used for sound localization. Consequently, HRTF renders 3D audio accurately in numerous immersive multimedia applications. Because HRTF is high-dimensional, complex, and nonlinear, it is a relatively large and intricate dataset, typically consisting of hundreds of thousands of samples. Storing HRTF requires a significant amount of storage space in practical applications. Based on this, high-dimensional, complex, and nonlinear HRTFs need to be compressed and reconstructed. In this study, inspired by the conventional common-pole/zero model, we propose a method for representing HRTF based on the common-pole/zero model and principal component analysis (PCA). Our method utilizes human auditory features and extends the traditional Common-Acoustical-Pole/Zero (CAPZ) method to estimate the common pole and zero coefficients across multiple subjects. Subsequently, the zero coefficients are compressed using the PCA procedure. Experimental results on the CIPIC database show that the compression ratio can reach 9.5% when the average spectral distortion is less than 2 dB.

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Keywords

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