A Distributed Speech Algorithm for Large Scale Data Communication Systems

Data-driven computing and using data for strategic advantages are exemplified by communication systems, and the speech intelligibility in communication systems is generally interrupted by interfering noise. This interference comes from the environmental noise, so we can reduce them intelligibility by masking the interested signal [1, 2]. An important work in communication systems is to extract speech from noisy speech and inhibiting background noise. In this paper, the subspace algorithm theory is introduced into a speech noise reduction system. We first analyze the principle of LMS adaptive speech noise reduction algorithm with the subspace algorithm, and then, we merge the subspace algorithm into the VS-LMS algorithm and propose a combined algorithm for an adaptive speech noise reduction system. Furthermore, we analyze the combined algorithm, which can decrease musical noise, as well as generate a suitable step-size factor to resolve the contradiction. This issue cannot be resolved by the current LMS algorithm [31], which has less convergence speed and larger residual noise than our system. Our simulation results demonstrate that our algorithm can get 3 to 10 times better than original algorithm in low SNR (-5 0db) and high SNR (0 ~ +5db).

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