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- Title
Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach.
- Authors
Tsui, Benjamin; Smith, William A. P.; Kearney, Gavin
- 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 into high frequency representations 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 were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.
- Subjects
TRANSFER functions; MACHINE learning; PREDICTION models; DEEP learning; INTERPOLATION
- Publication
Applied Sciences (2076-3417), 2020, Vol 10, Issue 17, p5764
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app10175764