On the preprocessing and postprocessing of HRTF individualization based on sparse representation of anthropometric features

Abstract

Individualization of head-related transfer functions (HRTFs) can be realized using the person's anthropometry with a pretrained model. This model usually establishes a direct linear or non-linear mapping from anthropometry to HRTFs in the training database. Due to the complex relation between anthropometry and HRTFs, the accuracy of this model depends heavily on the correct selection of the anthropometric features. To alleviate this problem and improve the accuracy of HRTF individualization, an indirect HRTF individualization framework was proposed recently, where HRTFs are synthesized using a sparse representation trained from the anthropometric features. In this paper, we extend their study on this framework by investigating the effects of different preprocessing and postprocessing methods on HRTF individualization. Our experimental results showed that preprocessing and postprocessing methods are crucial for achieving accurate HRTF individualization.

A Hybrid Algorithm for Predicting Median-Plane Head-Related Transfer Functions from Anthropometric Measurements

“…Data analysis methods have also been employed in the individualization of HRTFs. He et al synthesized HRTFs using a sparse representation with different pre-processes and post-processes trained from anthropometric parameters [22]. Furthermore, the radial basis neural network has been utilized in HRTF estimation based on anthropometric parameters and achieved promising performance [23].…”

Advancement of Individualized Head-Related Transfer Functions (HRTFs) in Perceiving the Spatialization Cues: Case Study for an Integrated HRTF Individualization Method

“…Tashev et al [13] supposed that the interaural time difference and HRTF magnitude possessed the same sparse relationship with the anthropometric features; they elicited a sparse vector from the anthropometric features, which could be used to recover the phase and magnitude of HRTFs. He et al [14] studied the accuracy of different preprocessing methods on the sparse reconstruction of HRTFs. Zhu et al [15] proposed another sparse based HRTF individualization method.…”

Surround by Sound: A Review of Spatial Audio Recording and Reproduction

“…The main idea is firstly to learn a sparse representation of a person's anthropometric features from the training set and then apply this sparse representation directly for HRTF synthesis. Further work shows that, in this method, the pre-processing and post-processing are crucial for the performance of HRTF individualization [58].…”