Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Low-order Spherical Harmonic HRTF Restoration using a Neural Network Approach

Version 1 : Received: 8 July 2020 / Approved: 10 July 2020 / Online: 10 July 2020 (08:58:11 CEST)

A peer-reviewed article of this Preprint also exists.

Tsui, B.; Smith, W.A.P.; Kearney, G. Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach. Appl. Sci. 2020, 10, 5764. Tsui, B.; Smith, W.A.P.; Kearney, G. Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach. Appl. Sci. 2020, 10, 5764.

Journal reference: Appl. Sci. 2020, 10, 5764
DOI: 10.3390/app10175764

Abstract

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

Subject Areas

Deep learning; Head Related Transfer Function (HRTF); Restoration; Ambisonics; Spatial Audio; Spherical harmonic; Audio signal processing; Denoising; Auto-Encoder; Neural Network

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