Modeling head-related transfer functions with spherical wavelets

Hu, Shichao; Treviño, Jorge; Salvador, César D.; Sakamoto, Shuichi; Suzuki, Yôiti

doi:10.1016/j.apacoust.2018.10.026

Cited by 7 publications

(11 citation statements)

References 24 publications

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“…Fig 4 are the manifold sampling for locating the spherical wavelet function at different levels. Although there should be multiple types of spherical wavelets, here in this study, spherical wavelets with the butterfly scheme are used in consistent with the previous study [30].…”

Section: B Spherical Wavelets Based Modelingmentioning

confidence: 99%

“…This study can be regarded as a following study of the previous method [30], in which the HRTF magnitudes are represented with spherical wavelets alone. The main difference is that our proposed method first captures the coarse structure based on a low order spherical harmonic representation with a very small number of parameters, and then applies spherical wavelets for modeling finer details which lies in the residual part.…”

Section: Joint Modeling Of Hrtf With Spherical Wavelets and Sphementioning

confidence: 99%

“…According to [23], the low-order spherical harmonic decomposition seems to provide a good performance for human sound localization. Therefore, it may be a better choice to model the coarse structure with spherical harmonics rather than spherical wavelets as shown in [30]. It can be summarized that the spherical harmonic decomposition is more efficient to represent the baseline performance for human sound localization, while the spherical wavelet representation is better to capture the finer details which may contribute to the perception of timbre [24].…”

Section: Joint Modeling Of Hrtf With Spherical Wavelets and Sphementioning

confidence: 99%

“…The spatial patterns of HRTFs consist of different spatial variations and shapes in different local regions that are called local features on the sphere. Recently, a new method is proposed to model the HRTFs with spherical wavelets, which shows a better performance of capturing the HRTF local features than the spherical harmonic method [29], [30]. Besides, locally modeling HRTF spatial patterns further allows for controlling the spatial resolutions in space with the expansion coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the two above methods, this study considers a joint modeling of HRTFs, in which the HRTF coarse structure is modeled as a low-order spherical harmonic representation, and the residual fine detail is captured with spherical wavelets methods. The main difference between the proposed method and the spherical wavelet method [30] is that the proposed method first approximate the coarse structure based on a low order spherical harmonics and use spherical wavelets to model the residual part, while the previous study [30] uses spherical wavelets alone for modeling both the coarse structure and local features. This proposed method is expected to have smaller approximation error than the spherical harmonic method when representing the HRTF magnitudes in space with same number of parameters.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Representation of Head-Related Transfer Functions With Combination of Spherical Harmonics and Spherical Wavelets

2019

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Recently, a modeling method for head-related transfer functions (HRTFs) in the spatial domain is proposed based on spherical wavelets. Because spherical wavelets are local functions on the sphere, HRTF local features can be efficiently represented by using a small number of analysis functions. This sparse representation method enables to control the spatial resolutions of a desired local region on the sphere with the expansion coefficients. Meanwhile, the conventional HRTF spatial variations models based on spherical harmonic decomposition, which is able to represent the HRTF coarse structure well with lower order models. In this paper, a modeling method of the HRTF spatial features is proposed by combining the spherical harmonics and spherical wavelets, which represents the coarse structure and spatial detail of the HRTFs, respectively. The numerical experiment results show that the proposed joint modeling method has smaller approximation errors within the frequency range of 7 kHz compared with 15 orders spherical harmonics modeling when the same number of parameters is used for representing the HRTF magnitude in all directions. In the comparisons with higher orders spherical harmonics modeling, the proposed joint modeling method yields smaller approximation errors within the frequency range of 20 kHz. Furthermore, the proposed joint modeling method is expected to help interpret the spatial cues in which the low orders spherical harmonic coefficients corresponds to head shadow effects while the spherical wavelet coefficients corresponding to the residual finer details may be interpreted as pinna effects. A tool for the HRTF spatial feature extraction is provided in this paper.INDEX TERMS Head-related transfer functions, 3D modeling, spherical harmonics, spherical wavelets.

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Section: B Spherical Wavelets Based Modelingmentioning

confidence: 99%

Section: Joint Modeling Of Hrtf With Spherical Wavelets and Sphementioning

confidence: 99%