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Related Experiment Video

Updated: May 8, 2026

A Method to Study Adaptation to Left-Right Reversed Audition

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

Parametric head-related transfer function modeling and interpolation for cost-efficient binaural sound applications.

German Ramos¹, Maximo Cobos

¹ITACA Institute, Universitat Politècnica de València, Valencia 46022, Spain.

The Journal of the Acoustical Society of America

|August 24, 2013

Summary

This study presents an efficient parametric method for modeling head-related transfer functions (HRTFs) using low-order filters. This approach enables cost-effective immersive audio and reduces HRTF database size.

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Last Updated: May 8, 2026

A Method to Study Adaptation to Left-Right Reversed Audition

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Published on: October 29, 2018

Pipeline for Planning and Execution of Transcranial Ultrasound Neuromodulation Experiments in Humans

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Published on: June 28, 2024

Area of Science:

Acoustics and Audio Engineering
Signal Processing
Virtual Reality Technologies

Background:

Parametric modeling of head-related transfer functions (HRTFs) is crucial for developing affordable immersive audio applications.
Existing methods may require high-order filters or extensive databases, limiting scalability and cost-effectiveness.

Purpose of the Study:

To introduce an efficient parametric method for modeling perceptually relevant HRTF features.
To enable the development of low-cost immersive sound applications through simplified HRTF representation.

Main Methods:

Utilized a low-order infinite impulse response (IIR) filter composed of cascaded second-order sections.
Employed conventional shelving and peak audio filters, with parameters (center frequency, gain, Q factor) iteratively adjusted.
Developed a method for synthesizing HRTFs at non-measured angles via parameter interpolation.

Main Results:

Achieved efficient, low-order binaural models for HRTFs.
Demonstrated successful synthesis of HRTFs for unmeasured spatial locations.
Significantly reduced the required HRTF database size.

Conclusions:

The proposed parametric method offers an efficient and effective way to model HRTFs.
This technique facilitates the creation of more accessible and scalable immersive audio experiences.
Reduced model complexity and database size contribute to lower costs in immersive sound development.