EigenHRTF

Audio plugin for accurate audio spatialization in the near-field.

Audio plugin for accurate audio spatialization in the near-field developed in Matlab. Based on an Eigen decomposition of HRTFs, the proposed method is capable of reducing a database comprising 6,344 measurements HRIR database proposed by Qu et al. [1] from 36.30 MB to 2.41MB (about a 15:1 compression ratio). Synthetic HRTFs in the compressed database were set to have less than 1dB spectral distortion between 0.1 and 16 kHz. Including 191 times more points (about million of measurements) by offline interpolation, compression still achieves 20% of compression. The differences between the compressed measurements with those in the original database do not seem to translate into degradation of perceptual location accuracy. The high degree of compression obtained with this method allows the inclusion of interpolated HRTFs in databases for easing the real-time audio spatialization.

Compilation:

To build this plugin, please use the buildPlugin function.

Parameters

To learn more about how to create this database, please refer to the following paper [2]. The sound field of the database is defined for:

• Distance: from 20 cm to 160 cm
• Elevation: from -40º to 90º (zenit)
• Azimuth: from 0º (front) to 359º (right and left are located at 90º and 270º respectively).
• Pinnae: 2 pinna sizes are defined (small and large).

Recommendations:

• Modification of the gain may change the subjective perception of distance. We strongly recommend do not modify this parameter. Use it at your own discretion.
• Best performance was achieved with MacOS.
• For windows, Visual Studio C++ 2019 is suggested to compile the plugin.

A video describing the process of this work can be found here: https://youtu.be/p2g4pgcYNXU

You can also try the code in the following repository:

https://github.com/arevaloarboled/HRTF-Eigen-decomposition

This is part of my submission for the AES MATLAB Plugin Student Competition.

REFERENCES

[1] T. Qu, Z. Xiao, M. Gong, Y. Huang, X. Li, and X. Wu, “Distance-Dependent Head-Related Transfer Functions Measured With High Spa-tial Resolution Using a Spark Gap,”IEEE Trans. on Audio, Speech &Language Processing, vol. 17, no. 6, pp. 1124–1132, 2009.

[2] Arevalo, Camilo & Villegas, Julián. (2020). Compressing Head-Related Transfer Function databases by Eigen decomposition. 1-6. 10.1109/MMSP48831.2020.9287134.