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    Determined Reverberant Blind Source Separation of Audio Mixing Signals

    Senquan Yang1, Fan Ding1, Jianjun Liu1, Pu Li1,2, Songxi Hu1,2,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3309-3323, 2023, DOI:10.32604/iasc.2023.035051

    Abstract Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix is derived using Newton gradient… More >

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