Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition

Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition
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DOI：
10.1109/access.2019.2906364
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发表时间：
2019
期刊：
IEEE Access
影响因子：
3.9
通讯作者：
Jing Wang;Min Liu;Xiang Xie;Jingming Kuang
Jing Wang;Min Liu;Xiang Xie;Jingming Kuang
中科院分区：
计算机科学3区
文献类型：
--
作者：
Jing Wang;Min Liu;Xiang Xie;Jingming Kuang

文献摘要

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头部相关传递函数（HRTF）在三维空间声系统中起着重要的作用。然而，直接应用大量的原始HRTF数据将涉及大量的计算负担，特别是对于高空间分辨率的单个HRTF。为了解决这个问题，我们提出了一种新的压缩方法（称为TT-Tucker）相结合的Tucker模型与张量序列分解的基础上开发的5阶HRTF张量模型的子空间的耳朵，受试者，方位角，仰角和频率。通过捕捉不同子空间之间隐藏的相互作用，可以将大量的HRTF数据分解为几个低参数因子，这些因子代表了HRTF的关键频谱信息。为了评估重建性能，在CIPIC HRTF数据库上进行了数值实验。结果表明，在相同的压缩比下（接近98%），该方法在谱失真和信失真比方面均优于常用的张量方法和标准主成分分析（PCA）方法。主观听音实验表明，TT-Tucker方法的性能更好，压缩重构后的HRTF在声音定位相似度上更接近原始HRTF。
Head-related transfer function (HRTF) plays an important role in three-dimensional spatial sound system. However, the direct application of a large amount of original HRTF data would involve a great deal of computational burden, especially for high-spatial-resolution individual HRTF. To address this problem, we propose a novel compression method (called TT-Tucker) combining Tucker model with tensor train decomposition based on a 5-order HRTF tensor model developed in subspaces of an ear, subject, azimuth, elevation, and frequency. Lots of HRTF data can be decomposed into several low-parametric factors representing the key spectrum information of HRTF by capturing the hidden interactions among different subspaces. To evaluate the reconstruction performance, the numerical experiments were conducted on the CIPIC HRTF database. Under the same compression ratio of nearly 98%, the results suggest that the proposed method has a better performance in spectral distortion and signal-to-distortion ratio than that of the usual tensor method and the standard method principal component analysis (PCA). Moreover, the subjective listening test shows that the TT-Tucker method performs better in that, the compressed and reconstructed HRTF is closer to the original HRTF in the sound localization similarity.