Effective Kalman Filtering Algorithm for Distributed Multichannel Speech Enhancement

   This project is designed based on the paper "Effective Kalman Filtering Algorithm for Distributed Multichannel Speech Enhancement".Kalman filtering is known as an effective speech enhancement technique. Many Kalman filtering algorithms for single channel speech enhancement were developed in past decades. However, the Kalman filtering algorithm for multichannel speech enhancement is very less. This paper proposes a Kalman filtering algorithm for distributed multichannel speech enhancement in the time domain under colored noise environment. Compared with conventional algorithms for distributed multichannel speech enhancement, the proposed KFADMSE algorithm has lower computational complexity and requires less computational resources. Simulation results show that the proposed algorithm is superior to the conventional algorithms for distributed multichannel speech enhancement in achieving higher noise reduction, less signal distortion and more speech intelligibility. Moreover, the proposed algorithm has a faster speed than several multi-channel speech enhancement algorithms.

Reference Paper-1: Effective Kalman filtering algorithm for distributed multichannel speech enhancement

Author’s Name: Xiguang Xu, Hua Qu, Jihong Zhao and Badong Chen

Source: IEEE -5th International Conference on Information Science and Technology (ICIST)

Year:2015

Reference Paper-2: A Novel Adaptive Fusion Scheme For Cooperative Spectrum Sensing

Author’s Name:  Jingxian Tu and Youshen Xia

Source: Elsevier

Year:2017

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SIMULATION VIDEO DEMO                                                                                                                                     


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