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.
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.
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.
Keywords
Deep learning; Head Related Transfer Function (HRTF); Restoration; Ambisonics; Spatial Audio; Spherical harmonic; Audio signal processing; Denoising; Auto-Encoder; Neural Network
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.