Paper Info
Title
Compression Of Head-Related Transfer Function Based On Tucker And Tensor Train Decomposition
Abstract
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.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2906364
IEEE ACCESS
Keywords
Field
DocType
Head-related transfer function, compression, tensor train, Tucker model
Compression (physics),Head-related transfer function,Computer science,Algorithm,Tensor train,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jing Wang103.72
Min Liu25616.44
Xiang Xie330.87
Jingming Kuang438860.48