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An Environment-Adaptation Based Binaural Localization Method

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Intelligence Science and Big Data Engineering (IScIDE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11266))

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Abstract

The degrading effect of reverberation on automatic sound localization is a challenging problem for many intelligent applications. Motivated by the environment-adaption ability of human auditory system, we modified the previous model by introducing the phase of room classification. 4 room types representing reverberation time from 0.32 to 0.89 s were used to evaluate the performance of the new method, and the result showed the localization accuracy could be improve about 1%–9%, depending on the sound location. The limitation and the further work of the method is analyzed and discussed.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China (No. 61473008, No. 61771023 and No. 11590773), and a Newton alumni funding by the Royal Society, UK.

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Authors and Affiliations

  1. Department of Machine Intelligence, Speech and Hearing Research Center, and Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing, 100871, China

    Tao Song & Jing Chen

Authors
  1. Tao Song
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  2. Jing Chen
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Corresponding author

Correspondence to Jing Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Shanghai Jiao Tong University, Shanghai, China

    Kai Yu

  3. Tsinghua University, Beijing, China

    Jiwen Lu

  4. Central China Normal University, Wuhan, China

    Xingpeng Jiang

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Song, T., Chen, J. (2018). An Environment-Adaptation Based Binaural Localization Method. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02697-4

  • Online ISBN: 978-3-030-02698-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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