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- W4380353904 abstract "An individualised head-related transfer function (HRTF) is essential for creating realistic virtual reality (VR) and augmented reality (AR) environments. However, acoustically measuring high-quality HRTFs requires expensive equipment and an acoustic lab setting. To overcome these limitations and to make this measurement more efficient HRTF upsampling has been exploited in the past where a high-resolution HRTF is created from a low-resolution one. This paper demonstrates how generative adversarial networks (GANs) can be applied to HRTF upsampling. We propose a novel approach that transforms the HRTF data for convenient use with a convolutional super-resolution generative adversarial network (SRGAN). This new approach is benchmarked against two baselines: barycentric upsampling and a HRTF selection approach. Experimental results show that the proposed method outperforms both baselines in terms of log-spectral distortion (LSD) and localisation performance using perceptual models when the input HRTF is sparse." @default.
- W4380353904 created "2023-06-13" @default.
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- W4380353904 date "2023-06-09" @default.
- W4380353904 modified "2023-09-27" @default.
- W4380353904 title "HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection" @default.
- W4380353904 doi "https://doi.org/10.48550/arxiv.2306.05812" @default.
- W4380353904 hasPublicationYear "2023" @default.
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