Journal on Communications ›› 2023, Vol. 44 ›› Issue (2): 82-93.doi: 10.11959/j.issn.1000-436x.2023027

• Papers • Previous Articles     Next Articles

Algorithm of underdetermined convolutive blind source separation for high reverberation environment

Yuan XIE1, Tao1 ZOU1, Weijun SUN2, Shengli XIE3   

  1. 1 School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    2 Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing, Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
    3 Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
  • Revised:2022-12-26 Online:2023-02-25 Published:2023-02-01
  • Supported by:
    The National Natural Science Foundation of China(62003095);The National Natural Science Foundation of China(52171331);Research and Development Plan of Key Fields in Guangdong Province(2019B01054002)

Abstract:

To separate the underdetermined convolutive mixture signals in the high reverberation environment, a novel algorithm of underdetermined convolutive blind source separation was proposed.Aiming at the influence of high reverberation environment, a global impulse response network was designed to weaken reverberation echo, improving signal quality.A new mathematical model of time-frequency mixing signals was established based on the global impulse response network.The global impulse response matrix which shortened the length of the traditional impulse response, reduced the approximation error of model transformation caused by high reverberation.The real-time update learning rules of model parameters were designed based on the theory of nonnegative matrix factorization, and the source signal separation problem was converted into the model parameter optimization problem, achieving blind source separation of mixing signals.Experimental results show that the proposed algorithm can effectively realize the blind source separation of Chinese and English speech and music signals, and the comparision with existing popular algorithms verified the superiority of the proposed algorithm.

Key words: blind source separation, underdetermined convolutive mixture, high reverberation environment, global impulse response network, nonnegative matrix factorization

CLC Number: 

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