一种用于降噪和上采样的HRTF机器学习方法
A Machine Learning Approach for Denoising and Upsampling HRTFs
AI总结：
本文提出了一种结合U-Net和AE-GAN的机器学习方法，用于降噪和上采样稀疏、嘈杂的HRTF测量，有效提升了HRTF上采样的精度。
摘要：
随着对逼真虚拟沉浸式音频需求的持续增长，头相关传递函数（HRTF）在其中起着关键作用。HRTF捕捉声音如何到达耳朵的过程，反映了独特的解剖特征，并增强了空间感知。已有研究表明，个性化HRTF可以提高定位准确性，但其测量仍耗时且需要无噪声环境。尽管机器学习已被证明可以减少所需测量点并因此减少测量时间，但仍需要受控环境。本文提出了一种方法来解决这一限制，通过提出一种新的技术，能够对稀疏、嘈杂的HRTF测量进行上采样。所提出的方法结合了HRTF Denoisy U-Net用于降噪和从三个测量点进行上采样的自编码生成对抗网络（AE-GAN）。所提出的方法实现了5.41 dB的对数频谱失真（LSD）误差和0.0070的余弦相似度损失，证明了该方法在HRTF上采样中的有效性。
英文摘要：
The demand for realistic virtual immersive audio continues to grow, with 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, a 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.
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