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HRTF Representation with Convolutional Auto-encoder

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

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Abstract

The head-related transfer function (HRTF) can be considered as some kind of filter that describes how a sound from an arbitrary spatial direction transfers to the listener’s eardrums. HRTF can be used to synthesize vivid virtual 3D sound that seems to come from any spatial location, which makes it play an important role in the 3D audio technology. However, the complexity and variation of auditory cues inherent in HRTF make it difficult to set up an accurate mathematical model with the conventional methods. In this paper, we put forward an HRTF representation modeling based on convolutional auto-encoder (CAE), which is some type of auto-encoder that contains convolutional layers in the encoder part and deconvolution layers in the decoder part. The experimental evaluation on the ARI HRTF database shows that the proposed model provides very good results on dimensionality reduction of HRTF.

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Acknowledgment

This work is supported by the National Key R&D Program of China (No. 2017YFB1002803), National Nature Science Foundation of China (No. 61701194, No. U1736206).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Wei Chen, Ruimin Hu & Xiaochen Wang

  2. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, 430072, China

    Ruimin Hu

  3. Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China

    Xiaochen Wang

  4. School of Mathematics and Computer Science, Jianghan University, Wuhan, China

    Dengshi Li

Authors
  1. Wei Chen
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  2. Ruimin Hu
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  3. Xiaochen Wang
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  4. Dengshi Li
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Corresponding author

Correspondence to Ruimin Hu .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Chen, W., Hu, R., Wang, X., Li, D. (2020). HRTF Representation with Convolutional Auto-encoder. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_49

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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