Improved Method for Individualization of Head-Related Transfer Functions on Horizontal Plane Using Reduced Number of Anthropometric Measurements

Hugeng, Wahidin Wahab; Gunawan, Dadang

doi:10.48550/arxiv.1005.5137

Cited by 6 publications

References 5 publications

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Spatial Audio Reproduction with Primary Ambient Extraction

He¹

2017

SpringerBriefs in Electrical and Computer Engineering

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Spatial Audio Reproduction with Primary Ambient Extraction

He¹

2017

SpringerBriefs in Electrical and Computer Engineering

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

Gan

Tan

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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.

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