Given that the mixing matrix of underdetermined blind source separation (UBSS) changes with the recording environment, offline UBSS methods encounter difficulty in satisfying the time-varying estimation demand. Therefore, in this work, an online tensor algorithm has been proposed to estimate the time-varying mixing matrix for separating an instantaneous linear underdetermined mixture. First, we construct a canonical polyadic tensor model by assuming individually correlated sources. Second, an online tensor algorithm is applied to decompose the canonical polyadic tensor model to ensure the accuracy of the time-varying mixing matrix. Finally, two types of data, including speech and biomedical signals, have been used to substantiate the effectiveness of the proposed algorithm in estimating the time-varying mixing matrix for UBSS. The results show that the developed online tensor algorithm is significantly superior to the conventional offline UBSS methods in terms of time consumption and accuracy.
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