Sparse orthogonal joint constrained multichannel non-negative matrix factorization algorithm for acoustic signal separation

Aiming at the problem of multichannel acoustic signal separation in complex environment, a sparse orthogonal joint constrained multichannel non-negative matrix factorization acoustic signal separation method is proposed. First, the cost function is constructed by sparse orthogonal joint constraints based on multichannel extended Itakura-Saito (IS) divergence, and auxiliary functions of signal sparse and signal orthogonal constraints are given to minimize the cost function. Then, through the design of iterative update rules, the basis matrix and coefficient matrix of multichannel non-negative matrix factorization for sparse and orthogonal optimization are obtained, and the influence of sparse orthogonal constraint on the sparsity and continuity of the basis matrix and coefficient matrix is discussed. Finally, based on the spatial characteristics of multichannel signals, non-negative matrix decomposition basis clustering is performed to obtain the separation results of multichannel Non-negative Matrix Factorization (NMF) acoustic signals. The experimental data tests of two-channel audio data and four-channel acoustic target separation show that the proposed algorithm improves the performance index Signal Distortion Ratio (SDR) by 0.84dB for audio data, and improves the SDR by 4.53dB for helicopter sound source data.