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Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings

Research Article

A Complex Neural Network Adaptive Beamforming for Multi-channel Speech Enhancement in Time Domain

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  • @INPROCEEDINGS{10.1007/978-3-030-99200-2_11,
        author={Tao Jiang and Hongqing Liu and Yi Zhou and Lu Gan},
        title={A Complex Neural Network Adaptive Beamforming for Multi-channel Speech Enhancement in Time Domain},
        proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings},
        proceedings_a={CHINACOM},
        year={2022},
        month={4},
        keywords={End-to-end Multi-channel Speech enhancement Complex operations},
        doi={10.1007/978-3-030-99200-2_11}
    }
    
  • Tao Jiang
    Hongqing Liu
    Yi Zhou
    Lu Gan
    Year: 2022
    A Complex Neural Network Adaptive Beamforming for Multi-channel Speech Enhancement in Time Domain
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-99200-2_11
Tao Jiang1,*, Hongqing Liu1, Yi Zhou1, Lu Gan2
  • 1: School of Communication and Information Engineering
  • 2: College of Engineering, Design and Physical Science, Brunel University
*Contact email: s190101065@stu.cqupt.edu.cn

Abstract

This paper presents a novel end-to-end multi-channel speech enhancement using complex time-domain operations. To that end, in time-domain, Hilbert transform is utilized to construct a complex time-domain analytic signal as the training inputs of the neural network. The proposed network system is composed of complex adaptive complex neural network beamforming and complex fully convolutional network (CNAB-CFCN). The real and imaginary parts (RI) of the clean speech analytic signal are used as training targets of the CNAB-CFCN network, and the weights of the CNAB-CFCN network are updated by calculating the scale invariant signal-to-distortion ratio (SI-SDR) loss function of the enhanced RI and clean RI. It is fundamentally different from the complex frequency domain single channel approach. The experimental results show that the proposed method demonstrates a significant improvement in end-to-end multi-channel speech enhancement scenarios.

Keywords
End-to-end Multi-channel Speech enhancement Complex operations
Published
2022-04-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99200-2_11
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