The demand for realistic virtual immersive audio continues to grow, con
Head-Related Transfer Functions (HRTFs) playing a key role. HRTFs capture how
sound reaches our ears, reflecting unique anatomical features and enhancing
spatial perception. It has been shown that personalized HRTFs improve
localization accuracy, but their measurement remains time-consuming and
requires a noise-free environment. Although machine learning has been shown to
reduce the required measurement points and, thus, the measurement time, UN
controlled environment is still necessary. This paper proposes a method to
address this constraint by presenting a novel technique that can upsample
sparse, noisy HRTF measurements. The proposed approach combines an HRTF Denoisy
U-Net for denoising and an Autoencoding Generative Adversarial Network (AE-GAN)
for upsampling from three measurement points. The proposed method achieves a
log-spectral distortion (LSD) error of 5.41 dB and a cosine similarity loss of
0.0070, demonstrating the method’s effectiveness in HRTF upsampling.

Questo articolo esplora i giri e le loro implicazioni.

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